DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1

CHAPTER 4

2

COASTAL WETLANDS

34
5

Coordinating Lead Authors

6

Hilary Kennedy (UK), Daniel M. Alongi (Australia), Ansarul Karim (Bangladesh)

7
8

Lead Authors

910

Guangcheng Chen (China), Gail L. Chmura (Canada), Stephen Crooks (USA), James G. Kairo (Kenya), Baowen
Liao (China), Guanghui Lin (China)

11
12

Contributing Author

13

Tiffany G. Troxler (TFI-TSU)

14
15

Review Editors

16

Nuria Marba Bordalba (Spain) and Georgi Karl Hiebaum (Bulgaria)

17

18

19

DRAFT 2013 Wetlands Supplement

4.1

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft

Contents

20

21

4.1

Introduction ........................................................................................................................................... 5

22

4.2

CO2 emissions and removals ............................................................................................................... 10

23

4.2.1

Forest management activities in mangroves .................................................................................... 10

24

4.2.1.1

Biomass .....................................................................................................................................1125

4.2.1.2

Dead organic matter ...................................................................................................................15

26

4.2.1.3

Soil carbon .................................................................................................................................17

4.2.2

27

Extraction ........................................................................................................................................ 1728

4.2.2.1

Biomass .....................................................................................................................................1929

4.2.2.2

Dead organic matter ...................................................................................................................2230

4.2.2.3

Soil carbon .................................................................................................................................24

31

4.2.3

Rewetting and revegetation of mangroves, tidal marshes and seagrass meadows .......................... 26

32

4.2.3.1

Biomass .....................................................................................................................................26

33

4.2.3.2

Dead organic matter ...................................................................................................................27

34

4.2.3.3

Soil carbon .................................................................................................................................27

35

4.2.4

Drainage in mangroves and tidal marshes ....................................................................................... 30

36

4.2.4.1

Biomass .....................................................................................................................................30

37

4.2.4.2

Dead organic matter ...................................................................................................................30

38

4.2.4.3

Soil carbon .................................................................................................................................30

39

4.3

40

Non-CO2 emissions ............................................................................................................................. 33

4.3.1

CH4 emissions from rewetted soils of mangrove and tidal marsh ................................................... 33

41

4.3.1.1

Choice of method.......................................................................................................................33

42

4.3.1.2

Choice of emission factors.........................................................................................................34

43

4.3.1.3

Choice of activity data ...............................................................................................................34

44

4.3.1.4

Uncertainty assessment..............................................................................................................34

45

4.3.2

N2O emissions during aquaculture use in mangroves, tidal marshes and seagrass meadows ......... 35

46

4.3.2.1

Choice of method.......................................................................................................................35

47

4.3.2.2

Choice of emission factors.........................................................................................................3648

4.3.2.3

Choice of activity data ...............................................................................................................3649

4.3.2.4

Uncertainty assessment..............................................................................................................3650

References ..................................................................................................................................................... 50

51

Annex 4A.1: Salinity-based defintions.......................................................................................................... 50

52

Annex 4A.2: Estimation methodology of aboveground mangrove biomass: higher tier methodology ......... 50

53

Annex 4A.3: Wood density of mangrove species ......................................................................................... 50

54

Annex 4A.4: Percent refractory carbon ......................................................................................................... 50

55

Appendix 4a.1: Future methodological development for estimating C export .............................................. 50

4.2

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

56

Equations57

58

Equation 4.1

Estimation of BCEF using BEF and wood densities.

59

Equation 4.2

Tier 1 estimation of initial change in C stocks with extraction (all C pools)

60

Equation 4.3

Initial change in carbon stocks with excavation (all C pools)

61

Equation 4.4

Tier 1 estimation of initial change in biomass C stocks due to extraction activities

62

Equation 4.5

Tier 1 estimation of initial change in DOM C stocks due to extraction activities

63

Equation 4.6

Tier 1 estimation of initial change in soil C stocks due to extraction activities

64

Equation 4.7

CO2 emissions on rewetted coastal wetland soils

65

Equation 4.8

CO2 emissions on drained organic and mineral soils

66

Equation 4.9

CH4 emissions in rewetted tidal marshes and mangroves

67

Equation 4.10 Direct N2O emissions from aquaculture use

68

Tables69

70

Table 4.1

Specified management activities in coastal wetlands

71

Table 4.2

Carbon fraction of aboveground mangrove forest biomass (tonnes C (tonnes d.m.)-1)

72

Table 4.3

Aboveground biomass in mangrove forests (tonnes d.m. ha-1)

73

Table 4.4

Aboveground biomass growth in mangrove forests (tonnes d.m. ha-1 yr-1)

74

Table 4.5

Ratio of belowground biomass to aboveground biomass (R) in mangroves forests

75

Table 4.6

Average density (tonnes m-3) mangrove wood

76

Table 4.7

Tier 1 default values for litter and dead wood carbon stocks

77

Table 4.8

Summary of Tier 1 estimation of initial changes in C pools for extraction activities

78

Table 4.9

Ratio of belowground biomass to aboveground biomass (R) for tidal marshes

79

Table 4.10

Ratio of belowground biomass to aboveground biomass (R) for seagrass meadows

80
81

Table 4.11

Soil C stocks for mangrove and tidal marsh on organic soils (tonnes C ha-1) for extraction
activities

82
83

Table 4.12

Annual emission factors (EF) associated with rewetting (EFREWET) on aggregated organic andmineral soils (tonnes C ha-1) at initiation of vegetation reestablishment

84
85

Table 4.13

Annual emission factors (EF) associated with drainage (EFDR) on aggregated organic andmineral soils (tonnes C ha-1 yr-1)

86
87

Table 4.14

Emission factors for Tier 1 estimation of rewetted land previously vegetated by tidal marshes
and mangroves

DRAFT 2013 Wetlands Supplement

4.3

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
88
89

Table 4.15

Figures90

91
92

Figure 4.1

Decision tree to indicate relevant section for Tier 1 estimation of greenhouse gas emissions andremovals due to specific management activities in coastal wetlands

Boxes

93

94
95
96

Emission factor (EFF) for N2O emission from aquaculture in mangroves, tidal marshes andseagrass meadowsBox 4.1

The following respresent examples of different management practices which may result in achange of a land-use category depending on how countries define mangroves and other coastal
wetlands

97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118

4.4

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

119

4.1 INTRODUCTION

120
121
122
123
124
125

This chapter provides guidance on estimating and reporting anthropogenic greenhouse gas (GHG) emissions
and removals from managed coastal wetlands. Coastal wetlands hold large reservoirs of carbon (C) in biomass
and especially soil, (Global stocks: Mangroves, ~8 Pg carbon; (Donato et al., 2011), tidal marshes, ~0.8 Pg
carbon (midrange; Pendleton et al. 2012), and seagrass meadows, 4.2 – 8.4 Pg carbon (Fourqurean et al., 2012).
Soil carbon originates largely in situ, from root biomass and litter, and can result in a significant pool in coastal
wetlands, especially when compared with terrestrial forests (Pidgeon 2009).

126
127
128
129
130
131
132

Coastal wetlands generally consist of organic and mineral soils that are covered, or saturated, for all or part of,
the year by tidal freshwater, brackish or salt water (Annex 4A.1) and are vegetated by vascular plants. The
boundary of coastal wetlands may extend to the landward extent of tidal inundation and may extend seaward to
the maximum depth of vascular plant vegetation. Countries need to develop a nationally appropriate definition ofcoastal wetland taking into account national circumstances and capabilities. This chapter refers specifically totidal freshwater1 and salt marshes, seagrass meadows, and mangroves. For non-tidal inland mineral wetland soils
refer to Chapter 5, this supplement.

Table 4.1 Specific Management Activities in Coastal Wetlands

Activity

Subactivity

Vegetation types affected

Activities relevant to CO2 emissions and removals
Forestmanagement
activities inmangroves

Planting, thinning, harvest, wood removal, fuelwood removal,
charcoal production.1

Mangrove2

Excavation to enable port, harbour and marina construction andfilling or dredging to facilitate raising the elevation of land,

Mangrove, Tidal marsh, Seagrassmeadow.

Aquaculture (construction)

Mangrove, Tidal marsh

Salt production (construction)

Mangrove, Tidal marsh

Drainage

Agriculture, forestry, mosquito control

Mangrove, Tidal marsh

Rewettingandrevegetation

Conversion from drained to saturated soils by restoring hydrology
and re-establishment of vegetation

Mangrove, Tidal marsh

Re-establishment of vegetation on undrained soils.

Seagrass meadow

Extraction

Activities relevant to Non-CO2 emissions
Aquaculture(use)

N2O emissions from aquaculture use

Mangrove, Tidal marsh, Seagrassmeadow

Rewettingandrevegetation

CH4 emissions from change to natural vegetation following
modifications to restore hydrology

Mangrove, Tidal marsh

1

Including afforestation and deforestation.
It is good practice to report mangroves in the appropriate national land-use category according to the national forest
definition and to consider when forest management activities may occur on mangroves classified under land-use categories
other than Forest land (similar types of examples in inventory reporting include wood harvest from orchards or otherperennial Cropland or harvest of trees from Wetlands).

2

133
134
135
136

It is good practice that inventory compilers determine country-specific boundaries for managed coastal wetlands,
recognizing national circumstances and country-specific definitions. Having applied the country-specificdefinition of coastal wetlands, the specific management activities (Table 4.1) need to be identified and emissions
1

At the present time, insufficient data are available to provide generic dafault data for C pools in tidal freshwater
swamps.

DRAFT 2013 Wetlands Supplement

4.5

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
137
138
139
140
141
142
143

and removals reported using the methodologies provided in this chapter. When identifying the nature andlocation of these activities, inventory compilers need only report GHG emissions or removals for activities
where the anthropogenic contribution dominates over natural emissions and removals. Management activities
resulting in extraction of soils, such as construction of aquaculture ponds, can result in large carbon dioxide (CO2)emissions in mangroves and tidal marshes. Nitrous oxide (N2O) emissions can be significant from aquaculture
activities. Rewetting of drained freshwater tidal systems increases methane (CH4) emissions, whilst increasing C
accumulation in mangrove biomass, dead wood and soils.

144
145
146
147
148
149
150
151
152
153

Coastal wetlands can potentially occur in any land-use category defined in the Good Practice Guidance for Land
Use, Land-use Change and Forestry (GPG-LULUCF) and the management activity may or may not result in aland use change (see Box 4.1). Regardless of whether a land-use change occurs or not, it is good practice toquantify and report significant emissions and removals (Table 4.1) resulting from management activities oncoastal wetlands. When occurring within the total land area of the country, GHG emissions and removals can bereported under any relevant land-use category to include the new subcategory Other Wetlands Remaining Other
Wetlands or Land Converted to Other Wetlands, to cover all potential reporting options. When activities andemissions occur on areas which are not included in the total land area, report those separately (refer to Chapter 7,
this supplement). In this way, countries need not be concerned with areas of coastal wetland, with small impacts
on C stock changes and emissions of non-CO2 gases, which are not included in the total land area,.

154
155
156
157

Readers are referred to Volume 4 of the 2006 IPCC Guidelines for many of the basic equations to estimate
greenhouse gas emissions, and new guidance is provided in this chapter, as necessary. The decision tree (Figure4.1) guides the inventory compiler to the appropriate estimation methodology for each of the specificmanagement activities covered in this Chapter.

158

COVERAGE OF THIS CHAPTER

159

This Chapter updates guidance contained in the 2006 IPCC Guidelines to:

160
161
162
163
164
165
166
167
168
169
170
171
172



provide default data for estimation of C stock changes in mangrove living biomass and dead woodpools for coastal wetlands at Tier 1.

This Chapter gives new: guidance for CO2 emissions and removals from organic and mineral soils for the management activities
of extraction (including construction of aquaculture and salt production ponds), drainage and rewetting
and revegetation.
 default data for estimation of anthropogenic CO2 emissions and removals for soils in mangrove, tidal
marsh and seagrass meadows.
 guidance for N2O emissions during aquaculture use guidance for CH4 emissions for rewetting and revegetation of mangroves and tidal marshes
The Appendix to this Chapter provides the basis for future methodological development to address:


173
174

Anthropogenic emissions and removals associated with dissolved or particulate carbon (DOC, POC)
loss during drainage as affected by tidal exchange.

175
176
177

For constructed wetlands that occur in coastal zones that are modified to receive and treat waste water, refer to
Chapter 6 (this supplement). Chapter 6 also covers semi-natural treatment wetlands which are natural wetlands
where wastewater has been directed for treatment but the wetland is otherwise unmodified.

178
179
180

While countries will follow their own national definitions of coastal wetlands, some general features that may
help in consistent identification can be found throughout this guidance. It is good practice to maintain consistentidentification of lands for the purpose of reporting.

4.6

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

181
182
183

BOX 4.1. THE FOLLOWING REPRESENT EXAMPLES OF DIFFERENT MANAGEMENT PRACTICES, WHICH MAY RESULT IN A
CHANGE OF A LAND-USE CATEGORY, DEPENDING ON HOW COUNTRIES DEFINE MANGROVES AND OTHER COASTALWETLANDS184

For Land remaining in a Land-use category:

185
186

Seagrass meadows or tidal marshes, classified as Wetlands and managed for aquaculture may still be reported
as Wetlands.

187
188

When tidal marshes are classified as Wetlands and managed for aquaculture, while the land also remains
classified as Wetlands.

189
190

Mangroves classified as Forest Land according to the national forest definition undergoes selective harvestingor biomass clearing may still be reported as Forest Land unless it undergoes a land-use change.

191
192
193
194

Mangroves do not meet all thresholds of a country’s definition of forest but are coastal wetlands with trees(and thus not classified as Forest Land) and are classified in another appropriate national land-use category. Insuch case, mangroves classified as Wetlands and subject to selective harvesting or biomass clearing may still
be reported as Wetlands.

195

Conversely, management activities may result in a change in reporting category; for example:

196
197

Seagrass meadows, initially classified as Wetlands and managed for aquaculture may be considered as
Settlement.

198
199

When tidal marshes are classified as Wetlands and are drained for agriculture and subsequently classified as a
new land-use category.

200
201

When mangroves are classified as Forest Land and undergo deforestation, or drainage and converted to a newland-use category.

202

MANAGEMENT ACTIVITIES IN COASTAL WETLANDS

203
204
205
206
207
208
209
210
211
212
213
214
215

Coastal wetlands that have been modified by anthropogenic activities are often reduced in area. Globally about
35% of the area of mangroves has disappeared since 1980, with a current global areal rate of loss of between 0.7
and 3% yr-1 (Pendelton et al., 2012). The management activities that have led to the majority of mangrove loss
include forestry activities (26%) and aquaculture, comprising the construction (and extraction of soil) for shrimp
ponds (38%) and fish farms (14%) (Vaiela et al., 2009). Other management activities may lead to the removal ofmangrove biomass without necessarily resulting in mangrove clearance i.e. harvesting for fuelwood, charcoaland construction. The current global areal rate of loss of tidal marsh is estimated to be between 1 and 2% yr-1
(Pendelton et al., 2012). Draining for agriculture, diking to separate marsh from tides, filling (after extraction)with imported sediment, and the extraction of soil during the construction of ponds for salt production arecommon management activities affecting tidal marshes. Seagrass meadows are experiencing a global areal rateof loss currently, of between 0.4 and 2.5% yr-1 (Pendelton et al., 2012). Globally, the main reasons for seagrass
loss are management activities such as dredging, leading to the excavation of soil to raise the elevation of land inlow lying areas and contribute to new land areas for settlement and aquaculture.

216
217
218
219
220
221
222

Revegetation efforts with mangroves, tidal marsh plants and seagrass, have been made worldwide to compensate
or mitigate for coastal wetland loss resulting from management activities (e.g. Bosire et al., 2008; Orth et al.,2011). Recovery of vegetation that characterised the coastal zone generally requires reinstatement of the preexisting environmental setting, such as rewetting (restored hydrology) to maintain saturated soils and facilitate
plant growth. All management activities generally, but not always, affect all vegetation types (i.e. mangroves,tidal marsh plants and seagrasses) or occur in all countries. Not all coastal wetlands will be managed. To identify
areas affected refer to respective sections on Activity Data and throughout this supplement.

223
224
225
226

DRAFT 2013 Wetlands Supplement

4.7

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
227
228

Figure 4.1 Decision tree to indicate relevant section for Tier 1 estimation of greenhouse gas emissions andremovals due to specific management activities in coastal wetlands2.

229
2

Extraction activities estimate CO2 emissions and removals for the initial change in C stocks that occur during
the year the extraction activities take place. Once the activity/activities is/are completed, these lands arecontinually tracked but CO2 emissions and removals are reported as zero at Tier 1. Forest management activities
in mangroves, drainage and rewetting are reported, based on the area of land where it occurs, lands tracked andCO2 emissions and removals subsequently reported in the annual inventory.

4.8

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

230
231
232

The following sections provide some general information on the specified management activities in coastal
wetlands that result in large anthropogenic emissions and removals.

233
234
235
236
237
238
239

Forest management activities in mangroves

240

Extraction

241

Extraction collectively refers to:

242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258

(A) Excavation of saturated soils leading to unsaturated (drained) soils and removal of biomass and dead organic
matter (DOM). Activities that lead to the excavation of soil often lead to loss of coastal wetlands. The
excavated or dredged soil is also commonly used to help develop coastal infrastructure where there is a needto raise the elevation of land in low lying areas and/or contribute to new land areas for settlement.
(B) Excavation during the “construction” phase of aquaculture and salt production ponds in mangroves and tidal
marshes followed by the “use” of these facilities.
Aquaculture and salt production are common activities in the coastal zone and similarly require excavation ofsoil and removal of biomass and DOM for construction. There is a range of aquaculture practices, but the mostimportant are fish farming and production from shrimp ponds (World Bank 2006). Salt production, from theevaporation of seawater, is also a widespread activity with sites along tropical and subtropical coasts worldwide,some of which have been producing salt for centuries (Oren 2009, Thiery and Puente 2002). In both activities,
ponds are constructed in mangroves and tidal marshes by clearing vegetation, levelling the soil and subsequently
excavating the surface soils to build berms where water is held. Depending on the type of aquaculture (intensive,extensive etc.) and the species stocked in the ponds (shrimp, fish) the soils can be excavated to make ponds of
0.5 m to 2.5 m depth (Cruz, 1997; Kungvankij et al., 1986; Wang 1990; Robertson and Phillips 1995). In asimilar manner the depth of salt production ponds can vary between depths of about 0.5 to 2.5m (e.g. OrtizMilan 2006, Madkour & Gaballah 2012).

259
260
261
262
263
264
265
266
267
268
269
270

Construction is only the first phase in aquaculture and salt production. The second phase, termed “use” is whenfish ponds, cages or pens are stocked and fish production occurs. In seagrass meadows, aquaculture ismaintained by housing fish in floating cages or pens that are anchored to the sediment (Alongi et al., 2009) andthese settings are considered during the use phase. N2O is emitted from aquaculture systems primarily as a byproduct of the conversion of ammonia (contained in fish urea) to nitrate through nitrification and nitrate to N2
gas through denitrification. The N2O emissions are related to the amount of nitrogen in the food provided to thefish and the fish production (Hu et al., 2012). When use of the aquaculture systems has been stopped, often due
to disease or declining water clarity (Stevenson et al., 1999), the systems transition to a final phasei.e.”discontinued”. All three phases (construction, use and discontinued) of aquaculture and salt production are
considered together with the other extraction activities, because the activity data are linked. However, only
construction is addressed at Tier 1 for CO2, with higher tiers addressing use and discontinued phases. For nonCO2, only the use phase is considered at Tier 1.

271
272
273
274
275
276

Rewetting and revegetation

277
278
279
280
281

Rewetting in mangroves and tidal marshes occurs where hydrologic modifications reverse drainage or remove
impoundments or other obstructions to hydrologic flow (e.g. levee breach). Also included in this activity are
mangroves and tidal marshes that have been created, typically by raising soil elevation or removing the upper
layer of upland soil or dredge spoil and grading the site until the appropriate tidal elevation is reached tofacilitate reestablishment of the original vegetation. Revegetation can occur by natural recolonisation, direct

Removal of wood occurs throughout the tropics where mangrove forests are harvested for fuelwood, charcoal,and construction (Ellison and Farnsworth 1996; Walters et al., 2008). The wood removal can range fromextensive forest clearing (which may lead to deforestation), to more moderate, selective harvesting of individual
trees, or to minimally invasive activities such as bark removal. Natural disturbances are another form of biomass
C stock loss. There may also be afforestation where mangrove replanting can take place on rewetted, or alreadysaturated, soils.

Rewetting is a pre-requisite for vegetation reestablishment and/or creation of conditions conducive to purposefulplanting of vegetation characteristic of coastal wetlands. This activity is also used to describe the management
activities designed to reestablish vegetation on undrained soils in seagrass meadows. Once the natural vegetation
is established, soil carbon accumulation is initiated at rates commensurate to those found in natural settings(Craft et al., 2002, 2003; Osland et al. 2012).

DRAFT 2013 Wetlands Supplement

4.9

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
282
283
284

seeding and purposeful planting. Alternatively, created wetlands with mangroves can be found where highriverine sediment loads lead to rapid sediment accumulation, so that previously sub-aqueous soils can beelevated above tidal influence. This naturally created land can be reseeded or purposefully vegetated.

285
286
287

The rewetting of tidal marshes and mangroves through reconnection of hydrology may lead to CH4 emissions(Harris 2010), particularly at low salinities, with an inverse relationship between CH4 emissions and salinity(Purvaja & Ramesh 2001; Poffenbarger et al., 2011).

288
289
290
291

In coastal wetlands where seagrass loss has occurred, due to anthropogenic activities, soils remain saturated.
Initiatives to allow revegeation can include natural or purposeful dispersal of seed or planting of seagrass
modules (Orth et al., 2011). These same techniques can also be used to create (rather than re-establish) seagrassmeadows (Jones et al., 2012).

292
293
294
295
296
297

Drainage

298

4.2

299
300
301
302
303
304
305
306
307
308

This section provides the methodology to estimate CO2 emissions and removals from human activities in coastal
wetlands comprising forest management activities in mangroves, extraction, drainage and rewetting on CO2emissions and removals. The methodological guidance provided here is consistent with methods for biomass andDOM in Volume 4 of the 2006 IPCC Guidelines and are in large part based on that methodological guidance : (1)for forest management activities in mangroves, methods for biomass and DOM are in large part based on
Chapter 4 of Volume 4; (2) for extraction activities, the methodological guidance is generally consistent withguidance for peat extraction Chapter 7 of Volume 4; and (3) for rewetting and drainage activities, updated
methodological guidance found in other Chapters of this Supplement is consistent with the methodologies
presented here. Activities covered by this chapter are described in Table 4.1. Separate guidance is provided on
estimation of changes in C stock from the five C pools.

309
310
311
312

Depending on circumstances, practices and definitions, specific coastal wetland management activities may or
may not involve a change in land-use. The guidance in this chapter needs to be applied regardless of thereporting categories. In particular, no recommendation is provided in relation to transition periods between land
use categories; countries can apply the existing transition period of appropriate land use categories.

313
314
315
316
317
318
319
320

Consistent with the 2006 IPCC Guidelines, the Tier 1 default approach assumes that the change in biomass and
DOM C stocks are zero on all lands except on Forest Land or on Cropland, Grassland and Wetlands withperennial woody biomass. On Forest Land and on Cropland, Grassland, or Wetlands with woody biomass, thewoody biomass and woody DOM pools are potentially significant and need to be estimated in a mannerconsistent with the guidance provided in Chapters 2 (generic methods), 4 (Forest Land), 5 (Cropland), 6(Grassland) and 7 (Wetlands) in Volume 4 of the 2006 IPCC Guidelines. Guidance provided here refers toEquations 2.7, 2.8 and the subsequent equations in Chapter 2 of the 2006 IPCC Guidelines which split the C
stock changes in the biomass pool or ΔCB into the various possible gains and losses.

321
322
323
324
325
326
327
328

If specific management activities in coastal wetlands (Table 4.1) are accompanied by a change in land use thatinvolves Forest Land or Cropland, Grassland or Wetlands with perennial woody biomass, changes in C stocks inbiomass, dead wood and litter pools are equal to the difference in C stocks in the old and new land-use categories(see Section 2.3.1.2, Chapter 2, Volume 4 of the 2006 IPCC Guidelines). These changes in C stock occur only inthe year of the conversion (extraction activities), or are uniformly distributed over the length of the transition
period (e.g. planting, harvesting). In soils the change in C stocks for extraction activities occurs in the year of
conversion, while for drainage, emissions persist as long as the soil remains drained or as long as organic matterremains, following the methodological guidance in this chapter.

329

4.2.1 Forest management activities in mangroves

Mangroves and tidal marshes have been diked and drained to create pastures, croplands and settlements since
before the 11th century (Gedan et al., 2009). The practice continues today on many coastlines. On some diked
coasts, groundwater of reclaimed former wetlands is pumped out to maintain the water table at the required level
below a dry soil surface while on other coasts drainage is achieved through a system of ditches and tidal gates.
Due to the substantial C reservoirs of coastal wetlands, drainage can lead to large CO2 emissions.

4.10

CO 2 EMISSIONS AND REMOVALSDRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

330
331
332
333
334
335
336
337

This section deals with CO2 emissions and removals associated with forest management activities in mangroves.
It is good practice to follow a country’s national definition of forest, but also to apply the appropriate guidance
when mangrove wetlands have trees, but that do not necessarily satisfy all thresholds of the national definition offorest. Depending on how the land is classified, forest management activities in mangroves may or may not leadto a change in land-use category (examples provided in Box 4.1). For estimation methodologies refer to thegeneric guidance provided in Chapter 2 of Volume 4 and more specific guidance in the relevant chapters of the2006 IPCC Guidelines for reporting CO2 emissions and removals for aboveground biomass, belowgroundbiomass and DOM (litter and dead wood).

338

4.2.1.1 B IOMASS

339
340
341
342
343
344
345
346
347
348

Biomass can be stored in mangroves that contain perennial woody vegetation. The default methodology for
estimating carbon stock changes in woody biomass is provided in Section 2.2.1, Chapter 2, Volume 4 of the2006 IPCC Guidelines. The change in biomass is only estimated for perennial woody vegetation of mangroves.
Changes in mangrove biomass may be estimated from either: 1) annual rates of biomass gain and loss (Equation
2.7, Chapter 2) or 2) changes in carbon stocks at two points in time (Equation 2.8, Chapter 2). The first approach(Gain-Loss method) can be used for Tier 1 estimation (with refinements at higher tiers) whereas the second
approach can be used for Tier 2 or 3 estimations. It is good practice for countries to strive to improve inventory
and reporting approaches by advancing to the highest possible tier given national circumstances. For coastal
wetlands with non-woody vegetation (i.e. seagrass meadows and many tidal marshes), increase in biomass stocks
in a single year is assumed equal to biomass losses from mortality in that same year leading to no net change.

349

CHOICE OF METHOD

350
351
352
353
354
355
356

Tier 1

357
358
359
360
361

If the land satisfies 1] a country’s definition of forest or 2] is a mangrove wetland with trees, and is managed forforest activities where land-use change has occurred or trees have been cleared, guidance is provided in “Section
2.3.1.2 Land Converted to a New Land-Use Category” and in the specific guidance in the relevant chapters ofVolume 4 of the 2006 IPCC Guidelines and applied using the default data provided in this chapter (Table 4.2 –4.6) and specific guidance below.

362
363
364

When either the biomass stock or its change in a category (or sub-category) is significant or a key category, it isgood practice to select a higher tier for estimation. The choice of Tier 2 or 3 methods depends on the types andaccuracy of data and models available, level of spatial disaggregation of activity data and national circumstances.

365
366
367
368

If using activity data collected via Approach 1 (see Chapter 3 of Volume 4 in the 2006 IPCC Guidelines), and it
is not possible to use supplementary data to identify land converted from and to the respective land category, theinventory compiler needs to estimate C stocks in biomass following Section 2.3.1.1 and specific relevant
guidance as indicated above.

369
370
371
372
373
374

Because a biomass conversion and expansion factor (BCEF) is not available for mangroves, when BCEF isapplied for determination of aboveground biomass from merchantable growing stock, for conversion of net
annual increment or for conversion of woody and fuelwood removal volume to aboveground biomass removal,
the same BCEF is applied and derived from wood density (Table 4.6) and a default value of BEF (Table
3A.1.10- Annex 3A.10 of the Good Practice Guidance for Land Use, Land-use Change, and Forestry) following
Equation 4.1 and as described in Box 4.2 of Chapter 4, Volume 4 of the 2006 IPCC Guidelines.

If the land satisfies 1] a country’s definition of forest or 2] is a mangrove wetland with trees, that nonetheless do
not meet the national definition of forest, and is managed for forest activities where no land-use change has
occurred, guidance is provided in “Section 2.3.1.1 Land Remaining in a Land-Use Category” and in the specificguidance in Volume 4, of the IPCC 2006 Guidelines and applied using the default data provided in this chapter(Table 4.2 – 4.6) and specific guidance below. Examples may include Forest Land to Forest Land, Wetlands to
Wetlands or Other Wetlands to Other Wetlands.

DRAFT 2013 Wetlands Supplement

4.11

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
375
376
377
378
379

EQUATION 4.1
ESTIMATION OF BCEF USING BEF AND WOOD DENSITIESBCEF = BEF • D(Section 2.3.1.1, Chapter 2 of the 2006 IPCC Guidelines)
where

380
381

BCEF = biomass conversion and expansion factor for conversion of growing stock, net annual increment orwood removals into aboveground biomass, aboveground biomass growth or biomass removals (tonnes m-3).

382
383

BEF = biomass expansion factor (dimensionless), to expand the dry weight of the merchantable volume of
growing stock, net annual increment or wood removals, to account for non-merchantable components.

384

D = wood density (tonnes m-3)

385
386
387
388
389
390
391
392

Tier 2

393
394
395
396
397
398
399
400
401
402
403
404
405

Tier 3406

CHOICE OF EMISSION/REMOVAL FACTORS

407
408
409
410
411
412
413
414

Tier 1

415
416
417
418

Tier 2

419
420
421
422
423

Tier 3As in Tier 1 the Gain-Loss can be applied using country-specific data. In addition, the Stock-Difference method
can also be applied using country-specific emission factors. If using the Stock-Difference method, countryspecific BEF or BCEF data or species specific wood density values (provided in Table 2 of Annex 4.2) could beapplied. For Tier 2, countries may also modify the assumption that biomass immediately following conversion toa new land-use category, or after mangrove trees are cleared, are zero. Refer to the relevant sections in Volume 4of the 2006 IPCC Guidelines for further guidance on Tier 2 methodologies for forest management activities inmangroves.
Tier 3 approach for biomass carbon stock change estimation allows for a variety of methods, including Tier 1
and 2 methods, to process based models that simulate the dynamics of biomass C stock changes. Countrydefined methodology can be based on estimates of aboveground biomass through use of allometric equations(Annex 4.2) or include detailed inventories based on permanent sample plots (Annex 4.2). Tier 3 could also
involve substantial national data on disaggregation by vegetation type, ecological zone and salinity. Tier 3
approaches can use growth curves stratified by species, ecological zones, site productivity and management
intensity. If developing alternative methods, these need to be clearly documented. Refer to the relevant sectionsin Chapter 4, Volume 4 of the 2006 IPCC Guidelines for further guidance on Tier 2 methodologies for forest
management activities in mangroves. Spaceborne optical and radar data can be used for mapping changes in theextent of mangroves and transitions to and from other land covers. Such techniques currently cannot routinely
provide estimate to a sufficient level of accuracy although this may become more feasible in the future (refer to
Activity data section).

For countries using the Gain-Loss method and a Tier 1 approach, the estimation of the annual carbon gains inliving biomass requires the following: carbon fraction of aboveground biomass, average aboveground biomass
per hectare, mean annual aboveground biomass growth, ratio of belowground biomass to aboveground biomass
and average wood density. The default values for these parameters are provided in Tables 4.2-4.6, respectively.
It is good practice to apply annual growth rates that lead neither to over- nor underestimates. Losses due to wood
removals, fuelwood removals and disturbances are also needed (refer to Choice of Activity Data for Tier 1 anduncertainty analysis in this section).
National data could include country specific values of any parameter used in the Tier 1 method or values that
permit biomass C stock changes using the Stock-Difference method. Refer also to the relevant sections ofVolume 4 of the 2006 IPCC Guidelines for further guidance.
Tier 3 methods may employ the use of data that are of higher order spatial disaggregation and that depend on
variation in salinity or further disaggregation of regional differences within a country. Forest growth rates of
specific age ranges could be applied. Refer also to the relevant sections of Volume 4 of the 2006 IPCC
Guidelines for further guidance.

4.12

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

424
Table 4.2 Carbon fraction of aboveground mangrove biomass (tonnes C (tonnes d.m.)-1) 2
Component%C

95% CI

Range

Leaves + wood1

45.1 (n = 47)

42.9, 47.1

42.2-50.2

1

Spain and Holt, 1980; Gong and Ong, 1990; Twilley et al., 1992; Bouillon et al., 2007; Saenger, 2002; Alongi et al., 2003; 2004; Kristensen
et al., 2008
2

This Table provides supplementary values to those presented in Table 4.3 chapter 4, volume 4 of the 2006 IPCC Guidelines.

425
426
Table 4.3 Aboveground biomass in mangroves (tonnes d.m. ha-1) 4
DomainRegionAboveground biomass

95%CI

Range

TropicalTropical Wet

192 (n=49)1

187, 2048.7-384

Tropical Dry

2

92 (n = 13)

88, 97

3.2-2013

66, 84

3.9-129

Subtropical75 (n= 10)

1

References: Golley et al., 1975; Christensen, 1978; Ong et al., 1982; Putz and Chan, 1986; Tamai et al., 1986; Komiyama et al., 1987, 1988,
2000, 2008; Lin et al., 1990; Mall et al., 1991; Amarasinghe and Balasubramaniam, 1992; Kusmana et al., 1992; Slim et al., 1996; Fromard
et al., 1998; Norhayati and Latiff, 2001; Poungparn, 2003; Sherman et al., 2003; Juliana and Nizam, 2004; Kirui et al., 2006; Kairo et al.,2008; Fatoyinbo et al. 2008; Camacho et al., 2011; Kauffman et al., 2011;Thant and Kanzaki, 2011.
2

References: Golley et al, 1962; Briggs, 1977; Suzuki and Tagawa, 1983; Steinke et al., 1995; Alongi et al., 2003; Medeiros and Sampoia,
2008; Khan et al., 2009.
3
References: Lugo and Snedaker, 1974; Woodroffe, 1984; Lee, 1990; Mackey, 1993; Tam et al., 1995; Saintilan, 1997; Ross et al., 2001;
Coronado-Molina et al., 2004; Simard et al., 2006; Fatoyinbo et al., 2008; Komiyama et al., 2008; Abohassan et al., 2012.
4This Table provides supplementary values to those presented in Table 4.7-4.9 Chapter 4, Volume 4 of the 2006 IPCC Guidelines.

427
428
Table 4.4 Aboveground biomass growth in mangrove forests (tonnes d.m. ha-1 yr-1)1, 2, 3
DomainRegionAboveground biomass growth

95%CI

Range

TropicalTropical Wet

9.9 (n=23)

9.4, 10.4

0.1-27.4

Tropical Dry

3.3 (n = 6)

3.1, 3.5

0.1-7.5

18.1 (n= 4)

17.1, 19.1

5.3-29.1

Subtropical
1

References: Ajonina 2008; Kairo et al., 2008; Alongi 2010

2
Biomass growth rates are from forests of varying age and such default values should only pertain to forests until the C biomass stock (Table
4.3) is reached.
3This Table provides supplementary values to those presented in Table 4.10 Chapter 4, Volume 4 of the 2006 IPCC Guidelines.

429
430
431
432
433
434
435
436
437
438

DRAFT 2013 Wetlands Supplement

4.13

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
Table 4.5 Ratio of belowground biomass to aboveground biomass (R) in mangroves forests4
DomainRegionTropical

R

95%CI

Range

Tropical Humid

0.49 (n=18)

10.47, 0.51

0.04-1.1

Tropical Dry

0.29 (n = 9)2

0.28, 0.30

0.09-0.79

0.96 (n= 18)3

0.91, 1.0

0.22-267

Subtropical
1

References: Golley et al., 1975; Tamai et al., 1986; Komiyama et al., 1987, 1988; Gong and Ong, 1990; Lin and Lu 1990; Tam et al., 1995;
Poungparn, 2003
2

References: Golley et al, 1962; Alongi et al., 2003; Hoque et al., 2010.

3

References: Briggs, 1977; Lin, 1989; Saintilan, 1997.

4This Table provides supplementary values to those presented in Table 4.4, Chapter 4, Volume 4 of the 2006 IPCC Guidelines

439
440
Table 4.6. Average density (tonnes m-3) mangrove wood1
Wood

EF

95% CI

range

n

0.703

0.650, 0.756

0.411-0.95885

Source: Global Wood Density Database http://datadryad.org/resource/doi:10.5061/dryad.234/1?show=full; Kauffman and Donato 2010.
Bosire et al., 2012
1

441
442

CHOICE OF ACTIVITY DATA

443
444
445
446
447
448

The vegetation type as well as soil type can be obtained from national wetland and soil type maps (if available)or the International Soil Reference and Information Centre; www.isric.org). Wetland distributions for mostcountries can be obtained from the RAMSAR web site (www.ramsar.org). When information is gathered frommultiple sources, it is good practice to conduct cross-checks to ensure complete and consistent representation
and avoid omissions and double-counting. All tiers require information on areas of forest management activities
in mangroves.

449
450

Tier 1
For Tier 1, these data can be obtained from one of the following sources (also see Annex 4A.3):

451

FAOSTAT http://faostat.fao.org/

452

Global Mangrove Database & Information System: http://www.glomis.com/

453

The UNESCO Mangrove Programme: http://www.unesco.org/csi/intro/mangrove.htm

454

Mangrove and the Ramsar Convention: http://www.ramsar.org/types_mangroves.htm

455

USGS Global Mangrove Project http://lca.usgs.gov/lca/globalmangrove/index.php

456

Mangrove.org: http://mangrove.org/

457

Mangrove Action Project: http://www.mangroveactionproject.org/

458

FAO Mangrove Management: http://www.fao.org/forestry/mangrove/en/

459

USGS National Wetlands Research Center: http://www.nwrc.usgs.gov/index.html

460

World Atlas of Mangrove: http://data.unep-wcmc.org/datasets/22

461

World Distribution of Coral Reefs and Mangroves: http://www.unep-wcmc.org

462
463
464

For Tier 1 estimation, FAO data sources can be used to estimate wood removal and fuelwood removal. Further
sources of activity data can be found in the relevant sections of Volume 4 of the 2006 IPCC Guidelines.
Additional resources can be found in IPCC (2010).

465
466
467

Global mangrove cover has been mapped by the United States Geological Service (USGS) for three epochs“1975” (1973-1983), “1990” (1989 – 1993), and “2000” (1997 -2000) and is available for download at
http://edcintl.cr.usgs.gov/ip/mangrove/download.php. Global distribution of Mangroves (V3.0, 1997) has been

4.14

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

468
469

compiled by UNEP World Conservation Monitoring Centre (UNEP-WCMC) in collaboration with theInternational Society for Mangrove Ecosystems (ISME).

470
471
472
473

The Kyoto & Carbon Initiative of the Japan Aerospace Exploration Agency (JAXA) Global Mangrove Watch
project, has used Synthetic Aperture Radar mosaics to create maps of global mangrove extent for the years 1995
and 2007-2010, and maps of annual changes in mangrove areas between the years 1995-2007, 2007-2008, 20082009 and 2009-2010. (http://www.eorc.jaxa.jp/ALOS/en/kyoto/mangrovewatch.htm).

474
475
476
477
478

Resources providing recent trends in coastal wetland area can help countries understand circumstances of thosetrends and what management activities contribute to them (FAO 2007; Green and Short 2003
http://archive.org/stream/worldatlasofseag03gree#page/n5/mode/2up;
Sifleet
et
al.
2011,
http://nicholasinstitute.duke.edu/publications?topics=34; Fatoyinbo & Simard 2013). If these links do not work,
either paste into your browser or do a simple web search for the resources or institution.

479

Sources providing international data can be verified, validated and updated data with national sources.

480
481
482
483
484
485
486
487

T ie rs 2 and 3
At Tiers 2 and 3, country-specific activity data is applied and at Tier 3, at the resolution required for Tier 3
methods. At higher tiers, information of these data may be obtained from local, state or regional government
department websites as many countries and regional government authorities report these data. Wood density
values (Annex 4.4) of specific species need to be applied at Tiers 2 and 3. Areas of extensive harvesting ofmangroves may be assessed with aerial imagery. When the ALOS-2 satellite is operational, generation of annualradarmosaics
andmangroveextentandchangemaps
isplanned(http://www.eorc.jaxa.jp/ALOS/en/kyoto/mangrovewatch.htm).

488

UNCERTAINTY ASSESSMENT

489
490
491
492
493
494

The major sources of uncertainty for all wetland types, but especially mangroves, are dominant species-specificdifferences in carbon content and differences due to forest age, species composition, intertidal location, soil
fertility and community structure. The confidence intervals presented in Tables 4.2 to 4.6, range from about 24%
to 200%. To reduce uncertainty, countries are encouraged to develop country- or region specific BEFs and
BCEFs. In case country- or regional-specific values are unavailable, it is good practice to check the sources ofdefault parameters and their correspondence with species present, as well as with the conditions in country.

495
496
497
498
499

The causes of variation of annual increment of mangrove growth include climate, site growth conditions, andsoil fertility. Artificially regenerated and managed stands are less variable than natural forests. One of the ways
to improve accuracy of estimates of these wetlands includes the application of country-specific or regional
estimates of growth stratified by the dominant species present. If the default values of growth increments areused, the uncertainty of the estimates need to be clearly indicated and documented.

500
501
502
503
504
505
506
507
508

For mangroves, data on commercial fellings are relatively accurate, although they may be incomplete or biased
due to illegal fellings and under-reported due to tax regulations. Traditional wood that is gathered and useddirectly, without being sold, is not likely to be included in any statistics. Countries must carefully consider theseissues. The amount of wood removed from forests after storm breaks and pest outbreaks varies both in time andvolume. No default data can be provided on these types of losses. The uncertainties associated with these losses
can be estimated from the amount of damaged wood directly withdrawn from the forest or using data on
damaged wood subsequently used for commercial and other purposes. If fuelwood gathering is treated separatelyfrom fellings, the relevant uncertainties might be high, due to the level of uncertainty associated with traditional
gathering.

509

4.2.1.2 D EAD O RGANIC M ATTER

510
511
512
513
514
515
516
517
518

The guidance for changes in the carbon pools in dead organic matter (DOM; dead wood and litter) in mangroves
provided in the 2006 IPCC Guideline remains unchanged. Dead roots ≤2 cm diameter are included in the soil
pool and not considered within the DOM pool. This fraction of dead roots turns over rapidly (Alongi 2009) withthe assumption of approximating steady state. DOM C stocks can vary depending on tidal inundation andfrequency, as well as soil oxidation and vegetation cover. Fine litter can be exported with tidal activity (Alongi2009) while a larger fraction of senesced woody biomass is buried or decomposed in-situ. In wetlands,
decomposition of DOM, especially wood, is slow (Robertson and Daniel 1989) and accumulates as soil organic
matter. Careful consideration of pools is needed in estimating inputs, outputs or changes of DOM C stocks to
avoid double-counting. Consistent with the 2006 IPCC Guidelines, it is good practice to consider DOM C stock

DRAFT 2013 Wetlands Supplement

4.15

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
519
520

changes when management activities in coastal wetlands result in changes in mangrove cover due to humaninduced impacts.

521

CHOICE OF METHOD

522
523
524
525
526
527
528

Tier 1

529
530
531
532
533

If the land satisfies 1] a country’s definition of forest or 2] is a mangrove wetland with trees, and is managed forforest activities where land-use change has occurred or trees have been cleared, guidance is provided in “Section
2.3.1.2 Land Converted to a New Land-Use Category” and in the specific guidance in the relevant chapters ofVolume 4 of the IPCC 2006 Guidelines and applied using the default data provided in this chapter (Table 4.7)
and specific guidance below.

534
535
536
537
538

Tier 2

539
540
541
542
543
544

Tier 3545

CHOICE OF EMISSION/REMOVAL FACTORS

546
547
548

Tier 1

549
550

Tier 2

551
552
553
554
555

Tier 3

If the land satisfies 1] a country’s definition of forest or 2] is a mangrove wetland with trees, that nonetheless do
not meet the national definition of forest, and is managed for forest activities, where no land-use change has
occurred, guidance is provided in “Section 2.3.1.1 Land Remaining in a Land-Use Category” and in the specificguidance in Volume 4, of the IPCC 2006 Guidelines and applied using the default data provided in this chapter(Table 4.7) and specific guidance below. Examples may include Forest land to Forest land, Wetlands to
Wetlands or Other Wetlands to Other Wetlands.

Estimation methodologies for Tier 2 can follow Tier 1 methods, but apply country-specific data. The StockDifference method (Chapter 4, Volume 4 of the 2006 IPCC Guidelines) could also be applied if countries havesample plot data from forest inventories for two points in time. Literature data or C databases may provide morefeasible and cost-effective data to apply this method.
Loss estimates of dead wood and litter exports due to tidal movement can also be considered (Appendix 4.1).
Tier 3 methods may further employ stratification by ecological zone or disturbance regime to reduce
uncertainties. It is good practice to report and sum changes in both dead wood and litter to obtain changes intotal dead organic matter. Additional Tier 3 guidance is provided in Chapter 4, Volume 4 of the 2006 IPPC
Guidelines.

Default values are provided in Table 4.7 of this Supplement for use in Tier 1 assessment of emissions andremovals.
Tier 2 methods using country-specific data if such country-specific data can be acquired at reasonable cost.
Tier 3 emission factors include model output and validation and disaggregated data sources. Field measurements
can be developed and used to inform and validate model output at Tier 3. For mangroves, Tier 3 methodologies
can employ empirical relationships to provide estimates of canopy litter fall and census of downed wood lying
on the forest floor.

556
TABLE 4.7 TIER 1 DEFAULT VALUES FOR LITTER AND DEAD WOOD CARBON STOCKS
Domain4.16

Ecosystem
type

Litter carbon stocks of maturemangrove stands (tonnes C ha-1)
with 95% CI

Dead wood carbon stocks of maturemangrove stands (tonnes C ha-1) with
95% CIDRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

Tropical/Subtropicalmangroves

0.7 (0-1.3)

10.7 (6.5-14.8)

Litter: Utrera-Lopez and Moreno-Casasola 2008, Liao et al 1990, Chen et al 2008, Richards et al 2011, Ramose-Silva et al
2007, Twilley et al 1986
Dead Wood: Kauffman et al 2011, Donato et al 2012, Allen et al 2000, Steinke et al 1995, Robertson et al 1989, Tam et al
1995, Krauss et al 2005

557
558

CHOICE OF ACTIVITY DATA

559
560
561
562
563
564
565
566

Tier 1

567
568
569
570

Tier 2 and Tier 3

571

UNCERTAINTY ASSESSMENT

572
573
574

Uncertainty assessment given in section 4.2.2.5 in Chapter 4 Volume 4 of the 2006 IPCC Guidelines identify
sources of uncertainty in estimates of C stock changes in the DOM pool of mangroves. Other sources of
uncertainty include output of DOM due to decomposition or tidal export.

575

4.2.1.3 S OIL CARBON

576
577
578
579
580
581

At Tier 1, if rewetting (section 4.2.3) or drainage activities (section 4.2.4) occur, follow the guidance for
estimating CO2 emissions and removals from soil C stock changes (Sections 4.2.3.3 and 4.2.4.3, respectively).
Otherwise, the Tier 1 default assumption is that soil CO2 emissions and removals are zero (EF=0) for forest
management activities in mangroves. This assumption can be modified at higher tiers. At higher tiers, it isrecommended to consider CO2 emissions from soils due to forest clearing in C stock estimations (Alongi et al.
1998).

582

4.2.2

583
584
585
586
587
588
589
590
591
592
593
594

Extraction refers collectively to the following activities (A) excavation: associated with dredging used to provide
soil for raising the elevation of land, or excavation to enable port, harbour and marina construction and filling,
and both (B) the construction of aquaculture ponds and (C) salt production ponds, where soil is excavated tobuild berms where water is held. Each of these extraction activities is associated with the removal of biomass,
DOM and soil, which results in significant emissions when their removal is from saturated (water-logged) to
unsaturated (aerobic) conditions (World Bank 2006). The Tier 1 methodology assumes that the biomass, DOM
and soil are all removed and disposed of under aerobic conditions where all carbon in these pools is emitted as
CO2 during the year of the extraction and that no subsequent changes occur. Tier 1 guidance is given here forreporting the intial changes in carbon (Table 4.1). Regardless of whether the extraction activities results in achange in land-use category, CO2 emissions and removals associated with extraction are the same, following
Equation 4.2 below. This approach follows the methodology applied for peat extraction in Chapter 7, Volume 4of the 2006 IPCC Guidelines.

C stock changes in DOM are generally not reported at Tier 1when management activities in coastal wetlands do
not result in changes in mangrove cover due to human-induced impacts (following guidance in Section 4.2.2.3 ofChapter 4, Volume 4 of the 2006 IPCC Guidelines), and thus no activity data are required. If a land-use change
has occurred resulting from an increase in woody biomass stock, it is good practice to report the change in DOM
C stock. For Tier 1 method, the annual rate of conversion to Forest Land or other Land-use categories withwoody mangrove biomass is required, following Section 4.3.2.3 of Chapter 4, Volume 4 of the 2006 IPCC
Guidelines. Activity data should be consistent with those used for estimating changes in carbon stock.
Inventories using higher tiers will require more comprehensive information on the establishment of new forests,
using climate, for example, as a disaggregating factor and at higher spatial and temporal resolution. Additional
resources can be found in IPCC (2010).

Extraction

595

DRAFT 2013 Wetlands Supplement

4.17

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
596
597
598

EQUATION 4.2
TIER 1 ESTIMATION OF INITIAL CHANGE IN C STOCKS WITH EXTRACTION (ALL C POOLS)ΔCEXT = ΔCexcav + ΔCaq-constr + ΔCsp-constr

599
600

Where

601

ΔCEXT = Changes in C stocks from all extraction activities; tonnes C

602
603

ΔCexcav = Initial change in biomass, DOM and soil carbon stocks from extraction due to excavation; tonnes
C

604
605

ΔCaq-constr = Initial change in biomass, DOM and soil carbon stocks from extraction during construction of
aquaculture ponds; tonnes C

606
607

ΔCsp-constr = Initial change in biomass, DOM and soil carbon stocks from extraction during construction ofsalt production ponds; tonnes C

608
609
610
611
612
613

Equation 4.2 is applied to the total area of coastal wetland where extraction activities take place. The terms
ΔCexcav, ΔCaq-constr, and ΔCsp-constr are estimated as ΔCCONVERSION (Equations 4.4 - 4.6) for intial change in carbon
stocks of each of the C pools for each of the respective activities comprising extraction. Equation 4.3 is applied
for each of the extraction activities (and A-C as described above) to estimate the intial change in stocks of eachof the C pools.

614
615
616

EQUATION 4.3

617

INITIAL CHANGE IN CARBON STOCKS WITH EXCAVATION (ALL C POOLS)

ΔCexcav = ΔCexcav-AB + ΔCexcav-BB + ΔCexcav-DOM + ΔCexcav-SO
where:

618

ΔCexcav = sum of the initial changes in C stock with excavation, tonnes C

619

ΔCexcav-AB = initial change in aboveground biomass C stock changes with excavation, tonnes C

620

ΔCexcav-BB = initial change in belowground biomass C stock changes with excavation, tonnes C

621

ΔCexcav-DOM = initial change in dead organic matter C stock changes with excavation, tonnes C

622
623

ΔCexcav-SO = initial change in soil C stock changes with excavation as annual CO2 emissions and removals,
tonnes C

624

where (v) denotes mangrove, tidal marsh and seagrass meadow,

625

At Tier 1,

626

ΔCexcav-AB + ΔCexcav-BB= ΔCB-CONVERSION (equation 4.4, section 4.2.2.1)

627

ΔCexcav-DOM = ΔCDOM-CONVERSION (equation 4.5, Section 4.2.2.2)

628

ΔCexcav-SO = ΔCSO-CONVERSION (equation 4.6, Section 4.2.2.3).

629
630
631
632

Equation 4.3 provides the formulation to estimate the initial change in carbon stock in each C pool for the
specific extraction activity, excavation. To estimate the initial changes in intial C stock change for these pools
for construction of aquaculture and salt production ponds, replace ΔCexcav with ΔCaq-constr and ΔCsp-constr inEquation 4.3, respectively.

633
634
635
636
637

The Tier 1 methodology assumes that the biomass, DOM and soil are all removed and disposed of under aerobic
conditions where all carbon in these pools is emitted as CO2 during the year of the extraction (consistent with theassumption applied for peat extraction in Section 7.2.1.1, Chapter 7, Volume 4 of the 2006 IPCC Guidelines)
and that no subsequent changes occur. Construction of aquaculture and salt production ponds is considered forthe vegetation types (v) of mangroves and tidal marsh only.

4.18

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

638
639
640
641
642

Table 4.8 summarizes the Tier level guidance provided for extraction activities, which deals with excavation in
general and excavation during the construction phase of aquaculture and salt production, in particular. Estimates
are not made at Tier 1 for possible CO2 emissions and removals while (1) fish ponds are stocked and salt
production is occuring (use phase) or (2) when the activity has ceased (discontinued phase), although they are
considered together with other extraction activities because the activity data are linked.

643
TABLE 4.8 SUMMARY OF TIER 1 ESTIMATION OF INITIAL CHANGES IN C POOLS FOR EXTRACTION ACTIVITIESC pools

Extraction
activities

Mangrovebiomass &DOM1
Excavation
Aquaculture
andSalt
Production

Construction
Use

Soils
Mangrove & Tidal Marsh

Seagrass

Organic

MineralMineral3

Tier 1

Tier 1

Tier 1

Tier 1

Tier 1

Tier 1

Tier 1
No guidance4

NA2

No guidance4

Discontinued

1

Removal of biomass resulting from extraction activities is estimated at Tier 1 level in mangroves only.
Extraction activity, aquaculture construction, is not applicable for fish pens or cages in seagrass meadows.
3
Tier 1 assumption is that all seagrass soils are mineral.
4
No suitable Tier 1methodologies are available for C pools during these phases/activities.
2

644
645

4.2.2.1 B IOMASS

646
647
648
649
650

This section addresses estimation of changes in living (above and belowground) biomass pools associated withextraction activities comprising excavation, and construction of aquaculture and salt production ponds in coastal
wetlands. For extraction in coastal wetlands with tidal marshes and seagrass meadows, changes in biomass
carbon stocks, are reported only Tier 2 or higher estimations. It is good practice to report the conversion ofaboveground and belowground biomass that occurs with extraction of mangroves.

651

CHOICE OF METHOD

652
653

Following Box 4.1 extraction may, or may not, result in a change in Land-use category, however, the same
methodologies apply for mangrove wetlands with forest regardless of how the land is classified.

654
655
656
657
658
659

Tier 1
Changes in carbon stock in living biomass during extraction are associated with clearing and removal ofvegetation. The area applied is that of a certain year in which the conversion occurs. Regardless of the land
category, the loss in biomass associated with extraction activities is estimated as ∆Cconversion following themethodology for peat extraction (Chapter 7, Volume 4 of the 2006 IPCC Guidelines), modified here as Equation
4.4:

660
661
662
663

EQUATION 4.4
TIER 1ESTIMATION OF INITIAL CHANGE IN BIOMASS C STOCKS DUE TO EXTRACTION ACTIVITIESΔCB-CONVERSION = ∑v,c{BAFTER * (1+R) − BBEFORE * (1+R)}v,c * CF *ACONVERTEDv,cWhere,

664

ΔCB-CONVERSION = Changes in biomass stock from conversion due to extraction activities; tonnes C

665
666

BAFTER = Stock in biomass per unit of area immediately after the conversion by vegetation type (v) andclimate (c); tonnes DM ha-1; default value = 0667

BBEFORE = Stock in biomass per unit of area immediately before the conversion tonnes d.m. ha-1

DRAFT 2013 Wetlands Supplement

4.19

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
668
669

R = ratio of belowground biomass to aboveground biomass by vegetation type (v) and climate (c); tonnes
DM belowground biomass (tonnes d.m. above ground biomass)-1.

670

CF = carbon fraction of dry matter, tonnes C (tonnes d.m.)-1

671

ACONVERTED = Area converted to other land-use category by vegetation type (v) and climate (c); ha

672
673
674
675
676

The Tier 1 methodology assumes that the biomass is removed and disposed of under aerobic conditions where all
carbon is emitted as CO2 during the year of the extraction and that no subsequent changes occur. At Tier 1,
initial change in C stocks of biomass {BAFTER * (1+R) − BBEFOREv * (1+R)}v,c is assumed to be zero for coastal
wetlands without perennial biomass or trees. For mangrove wetlands with perennial biomass or trees, the stock
after the conversion (BAFTER) at Tier 1 is taken to be zero.

677
678
679
680
681
682
683
684

Tier 2
At Tier 2, changes of C stock in living aboveground biomass of tidal marsh and seagrass meadow vegetation canbe estimated and reported for the specified activities employing the equation for ΔCB-CONVERSION, using countryspecific emission factors and default values for R given in Tables 4.9 and 4.10, in conjunction with countryspecific data on aboveground biomass. At Tier 2, the Gain-Loss or Stock-Difference methods can be applied toestimate biomass C stock changes of mangrove in lands where extraction activities (aquaculture and salt
production) are discontinued (i.e. regrowth). Tier 2 approaches could also include evaluation of the assumptionof instantaneous oxidation of the converted biomass pool.

685
686
687

Tier 3688

CHOICE OF EMISSION/REMOVAL FACTORS

689
690
691
692

Tier 1

693
694
695
696
697
698

Tier 2

In Tier 3, estimation could include methods to incorporate data on the fraction of biomass C stock that is retained
under saturated conditions to improve estimation of proportion of C that is oxidized.

Default data for Tier 1 method is provided for mangroves in Tables 4.2-4.6, Section 4.2.1, including
aboveground biomass C stock, C fraction and belowground to aboveground ratio, for the different climate
domains and regions, where applicable.
Under Tier 2, countries apply country specific data to estimate changes in C stock in aboveground biomass. The
conversion of aboveground and belowground biomass that occurs with extraction activities from tidal marsh andseagrass meadows may be estimated using Tables 4.9 and 4.10 for tidal marshes and seagrass meadowsrespectively. These data are to be used in conjunction with the carbon fraction of dry matter alongside countryspecific data on aboveground biomass stock.

699
700
701
702
703
704
705
706
707
708
709
710
711

4.20

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

TABLE 4.9 RATIO OF BELOWGROUND BIOMASS TO ABOVEGROUND BIOMASS (R) FOR TIDAL MARSHES
Domain
R
95%CI
Range
n
Mediterranean1
subtropical2

temperate fresh tidal
temperate

3

4

3.63

3.56, 3.7

1.09-7.15

5

3.65

3.56, 3.74

2.23-9.41

5

1.15

1.12, 1.18

0.36-3.85

7

2.11

2.07, 2.15

0.33-10.15

17

1

Scarton et al . 2002; Neves et al. 2007; Boyer et al. 2000

2

Lichacz et al. 1984; da Cunha Lana et al. 1991

3

Birch and Cooley1982; Whigham et al.1978

4

Kistritz et al 1983; Hussey and Long 1982; Smith et al. 1979; Dunn 1981; Connor and Chmura 2000; Gross et al. 1991;
Whigham et al.1978; Elsey-Quirk et al. 2011; Adams et al. 2012

TABLE 4.10 RATIO OF BELOWGROUND BIOMASS TO ABOVEGROUND BIOMASS (R) FOR SEAGRASS MEADOW
DomainR

95%CI4

Range

n

1

1.7

1

1.5, 1.9

0.05 – 25.62

396

2.4

2

2.3, 2.6

0.07 – 16.8

391

1.3

3

1.1, 1.5

0.14 – 13.8

91

Tropical2

Subtropical
Temperate

3

1

Aioi & Pollard 1993, Brouns 1985, Brouns 1987, Coles et al. 1993, Daby 2003, Devereux et al. 2011, Fourqurean et al. 2012, Halun etal. 2002, Holmer et al. 2001, Ismail 1993, Lee 1997, Lindeboom & Sandee 1989, McKenzie 1994, Mellors et al. 2002, Moriarty et al.
1990, Nienhuis et al. 1989, Ogden & Ogden 1982, Paynter et al 2001, Poovachiranon & Chansang 1994, Povidisa et al. 2009, Rasheed
1999, Udy et al. 1999, van Lent et al. 1991, van Tussenbroek 1998, Vermaat et al. 1993, Vermaat et al. 1995, Williams 1987.
2

Aioi 1980, Aioi et al. 1981, Asmus et al. 2000, Bandeira 2002, Boon 1986, Brun et al 2009, Collier et al. 2009, de Boer 2000, Devereux
et al. 2011, Dixon & Leverone 1995, Dos Santos et al. 2012, Dunton 1996, Fourqurean et al. 2012, Hackney 2003, Herbert andFourqurean 2009, Herbert & Fourqurean 2008, Holmer & Kendrick 2012, Jensen & Bell 2001, Kim et al. 2012, Kirkman & Reid 1979,
Kowalski et al. 2009, Larkum et al. 1984, Lee et al. 2005, Lee et al. 2005b, Lipkin 1979, Longstaff et al. 1999, Masini et al. 2001,
McGlathery et al. 2012, mcMahan 1968, Meling-Lopez & Ibarra-Obando 1999, Mukai et al. 1979, Paling & McComb 2000, Park et al.
2011, Powell 1989, Preen 1995, Schwarz et al. 2006, Stevensen 1988, Townsend & Fonseca 1998, Udy & Dennison 1997, van HouteHowes et al. 2004, van Lent et al. 1991, van Tussenbroek 1998, Walker 1985, West & Larkum 1979, Yarbro & Carlson 2008.
3
Agostini et al. 2003, Cebrian et al. 2000, Fourqurean et al. 2012, Hebert et al. 2007, Holmer & Kendrick 2012, Larned 2003, Lebreton etal. 2009, Lillebo et al. 2006, Marba & Duarte 2001, McRoy 1974, Olesen & Sand-Jensen 1994, Rismondo et al. 1997, Sand-Jensen &Borum 1983, Terrados et al. 2006

495%CI of the geometric mean

712
713
714
715
716

Tier 3717

CHOICE OF ACTIVITY DATA

718
719
720
721
722
723
724
725
726
727
728
729
730
731
732

Extraction: Submissions of licenses for prospecting and exploitation and associated environmental impact
assessments (EIAs) can be used to obtain areas under extraction activities. Relevant regulation for extraction canbe found at international and national levels. International regulation is covered by the UN Convention on the
Law of the Sea (UNCLOS) 1982 (www.un/org/Depts/los/index.htm). Contracting Parties are under the
obligation to publish/communicate reports on monitoring and assessment of potential harmful effects ofextraction. The OSPAR Convention 1992 (www.ospar.org) provides guidance for programmes and measures forthe control of the human activities in the North-East Atlantic region. The “Agreement on Sand and Gravel
Extraction provides that authorisation for extraction of marine soils from any ecologically sensitive site should
be granted after consideration of an EIA. The HELSINKI Convention 1992 (www.helcom.fi) covers the Baltic
Sea Area and requires EIAs to be carried out as part of the extraction process and that “monitoring data” and“results of EIA’s…be made available for scientific evaluation”. The Barcelona Convention 1995
(www.unepmap.org), covers the regulatory framework for the Mediterranean. The ICES Convention 1964
(www.ices.dk) provides data handling services to OSPAT and Helsinki Commissions. An overview of theregulation of marine aggregate operations in some European Union Member States is reported in in Radzevicius
et al. (2010) and includes relevant EC Directives and national legislation/regulation. Other such sources ofField measurements can be developed and used to inform and validate model output at Tier 3. It is expected thatdata improvements for excavation activities such as ground-truth estimates of overall area impacted, the depth atwhich removal of biomass has occurred, or the fraction of biomass removal, could be used to develop and verify
models.

DRAFT 2013 Wetlands Supplement

4.21

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
733
734
735

activity data include, for example, statistics on sand and gravel extraction for the OSPAR martime area (e.g.
www.ospar.org/documents/dbase/publications/p0043 ) as well as information on sand & gravel activities andrelated statistics for North Sea Continental Shelfs & UK waters (http://www.sandandgravel.com/).

736
737
738
739

Aquaculture and salt production: Annual data (1950 – present) providing statistics on aquaculture production
is collated by the FAO Fisheries and Aquaculture Department. Additional data on type aquaculture (e.g.
freshwater or brackish) and area under production is summarized in country profiles enabling stratification of
aquaculture into those occurring in coastal wetlands (http://www.fao.org/fishery/countryprofiles/search/en).

740
741
742
743
744
745
746
747

Similar project information for salt production activities can be obtained from the Salt Institute atwww.saltinstitute.org. As local regulations typically apply for developing new aquaculture activites (i.e licensing,permitting), regulations also typically apply to report such activities to the Ministry of Fisheries and Marine
Affairs (or country equivalent). For example an aquaculture farm needs to get a license (or permission) to
operate. Depending on the country, it is given by the regional (e.g. in Spain it is the autonomic -e.g. Balearicgovernment who approves it) or local (e.g. at Bolinao, The Philipines) and maybe in others the national
government. For example, in Indonesia local government must be consulted on land use change including
aquaculture pond construction and are obliged to report activities to the Ministry of Fisheries and Marine Affairs.

748
749

Literature sources can also provide national area change statisitics from aerial photographs of ponds or structures
used for aquaculture and salt production.

750
751
752

A map of available tidal marsh distribution (with area data) is in production by the World Conservation andMonitoring Center, http://data.unep-wcmc.org/, currently holding layers for Europe, the United States, Australia
and China. It is the intent to expand mapping of tidal marsh to global coverage.

753
754
755
756
757
758
759
760
761
762

A map of global distribution of seagrasses (V2.0, 2005) is also available at the World Conservation andMonitoring Center (WCMC) (http://data.unep-wcmc.org/) and prepared in collaboration with Dr. Frederick T.
Short.
Other
regional
andnational
maps
are
also
available,e.g.
http://www.ospar.org/documents/dbase/publications/p00426_zostera_beds. A tabulated list of web sites forexisting
seagrassmonitoring
programmes
is
given
in
Borum
et
al.,
(2000),
http://www.seagrasses.org/handbook/european_seagrasses_high.pdf.

763

UNCERTAINTY ASSESSMENT

764
765
766
767
768
769
770
771
772
773
774

For uncertainty assessment for mangroves, see Section 4.2.1 (this chapter). The uncertainties involved in
extraction and mangroves also follow those outlined in Section 4.3.1.5 of Chapter 4, Volume 4 of the 2006 IPCC
Guidelines. Variability in tidal marsh biomass will be due to differences in dominant species and competition
between species, as well as salinity of flood waters, frequency of tidal flooding and climate. For example, thehigh biomass in Mediterranean climates is due to the frequent dominance of perennial shrubs. For all vegetation
there can be considerable yearly variability in production of biomass and seasonal variability in standing biomass
that contributes to uncertainty in ratios of aboveground-belowground ratios. Most empirical data are available
from temperate regions and North America and there are limited data available for tidal freshwater and boreal
and subtropical tidal marshes. The average belowground to aboveground biomass for seagrass is variable
depending on the dominant species, and fertility of the soil. The data are mainly derived from observations along
the coasts of North America, Western Europe and Australia. Data were scarce from South America and Africa.

775

4.2.2.2 D EAD O RGANIC M ATTER

776
777
778
779

Previously saturated DOM, which is exposed to aerobic conditions, can contribute to large sources of CO2
emissions from extraction activities. Consistent with the 2006 IPCC Guidelines for Forest Land, in coastal
wetlands, it is good practice to consider DOM C stock changes when extraction activities result in changes inmangrove cover due to these human-induced impacts.

780

CHOICE OF METHOD

781

Tier 1

These data sources, and those provided in Section 4.2.1.1, can be used in conjunction with activity data
described above to improve estimations of areas of mangroves, tidal marsh and seagrass meadow undergoing
extraction activities.

4.22

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

782
783
784
785
786
787
788
789

During extraction activities, existing DOM pools may be reduced due to zero as vegetation is cleared andremoved at the same time no new C enters the DOM pool. At Tier 1, changes in carbon stock in DOM in tidal
marshes and seagrass meadows are assumed to be zero. It is noted, however, that extraction activities that resultin vegetation or soil disturbance in tidal marsh with perennial woody biomass may have significant impacts on C
emissions and removals and it is good practice for country specific methods to be developed to cover these cases,
if feasible. Regardless of the land category, the loss in DOM associated with extraction activities is estimated as∆Cconversion following the methodology applied for peat extraction (Chapter 7, Volume 4 of the 2006 IPCC
Guidelines), modified here as Equation 4.5:

790
791
792
793
794

EQUATION 4.5
TIER 1ESTIMATION OF INITIAL CHANGE IN DOM C STOCKS DUE TO EXTRACTION ACTIVITIES.

ΔCDOM-CONVERSION = ∑v(DOMAFTER − DOMBEFORE)v · ACONVERTEDv
Where,

795
796

ΔCDOM-CONVERSION = Initial changes in DOM stock from conversion due to extraction activities byvegetation type (v) and climate (c); tonnes C

797
798

DOMAFTER = Stock in DOM per unit of area immediately after the conversion by vegetation type (v) tonnes
d.m. ha-1; default value = 0

799
800

DOMBEFORE = Stock in DOM per unit of area immediately before the conversion by vegetation type (v)tonnes d.m. ha-1

801

CF = carbon fraction of dry matter, tonnes C (tonnes d.m.) -1

802

ACONVERTED = Area converted to other land-use category by vegetation type (v); ha

803
804
805

The Tier 1 methodology assumes that the DOM is removed and disposed of under aerobic conditions where all
carbon is emitted as CO2 during the year of the extraction and that no subsequent changes occur. The choice of
method follows that in Section 4.2.2.

806
807
808
809

Tiers 2 and 3

810

CHOICE OF EMISSION/REMOVAL FACTORS

811
812
813
814

Tier 1

815
816
817
818
819

Tier 2

820
821

Tier 3822

CHOICE OF ACTIVITY DATA

823
824

Choice of activity data follows from guidance above provided in Section 4.2.2.1. The area in which theextraction activities occur will be the same area applied for each C pool, especially forest biomass.

The choice of method follows that in Section 4.2.2. For these management activities that impact DOM pools in
tidal marshes with perennial or woody biomass, Tier 2 and higher estimation methods are recommended andthese values reported.

Default values of DOM carbon stock (for dead wood and litter) for mangroves are provided in Table 4.7 of thisSupplement for use in Tier 1 estimations. In tidal marsh and seagrass meadows the Tier 1 assumption is thatcarbon stocks in the DOM pools resulting from extraction activities are zero.
At Tier 2, the assumption that all DOM lost in the year of conversion is oxidized can be reassessed. Tier 2
assumption of zero for DOM after can also be assessed. It is good practice for countries, in such cases, to use
national estimates for DOM C stocks for mangroves and tidal marshes with perennial biomass, if such countryspecific data can be acquired at reasonable cost
Tier 3 emission factors include model output and validation and disaggregated data sources.

DRAFT 2013 Wetlands Supplement

4.23

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
825

UNCERTAINTY ASSESSMENT

826
827
828

The discussion on uncertainty outlined in Section 4.3.2.5 of Chapter 4, Volume 4 of the 2006 IPCC Guidelines isalso relevant for extraction of mangroves. Management activities in tidal marshes and seagrass meadows(without woody, perennial biomass) do not result in changes in DOM.

829

4.2.2.3 S OIL CARBON

830
831
832
833

Extraction activities that occur within coastal wetlands can influence organic and mineral stocks of C in soils andboth soil types are covered at Tier 1(Table 4.11). During extraction activities, the stock of soil C that is removed
depends on the soil type (i.e. C stock is higher in organic soils). For Tier 1 estimation, in the absence of soil map
data or other resources to differentiate soil type, the following assumptions can be applied:

834

i. Assume that soils in which seagrass grow are mineral.

835
836
837
838

ii. Assume all soils, regardless of dominant vegetation in or at the mouth of estuaries or adjacent to any river
characterised by a large and/or mountainous catchment and high flow, are mineral. For all othermangroves and tidal marshes the soils are organic. See Durr et al. (2011) for additional national levelguidance.

839
840

iii. If soils cannot be dissagregated into organic and mineral, use the aggregated default data given in Table
4.11

841

CHOICE OF METHOD - ORGANIC AND MINERAL SOILS

842
843
844
845

Tier 1
Regardless of the land category, the loss in soil carbon associated with extraction activities is estimated as∆Cconversion following the methodology applied for peat extraction (Chapter 7, Volume 4 of the 2006 IPCC
Guidelines), modified here as Equation 4.6

846
847
848
849

EQUATION 4.6
TIER 1ESTIMATION OF INITIAL CHANGE IN SOIL C STOCKS DUE TO EXTRACTION ACTIVITIES.

ΔCSO-CONVERSION = ∑v,s(SOAFTER − SOBEFORE)v,s · ACONVERTEDv,s
Where,850
851

ΔCSO-CONVERSION = Initial changes in soil carbon stock from conversion due to extraction activities byvegetation type (v) and soil type(s); tonnes C

852
853

SOAFTER = Carbon stock in soil per unit of area, immediately after the conversion, by vegetation type (v)and soil type (s); tonnes C ha-1; default value = 0

854
855

SOBEFORE = Carbon stock in soil per unit of area, immediately before the conversion, by vegetation type (v)and soil type (s); tonnes C ha-1

856

ACONVERTED = Area converted to other land-use category by vegetation type (v) and soil type (s); ha

857
858
859

At Tier 1, soil extraction depth to 1m approximates the mid-range of the extraction depth for construction of
aquaculture and salt production ponds (see extraction activities in section 4.1). Countries may modify theassumption of 1m extraction depth at higher tiers.

860
861
862
863
864
865
866
867
868
869

The Tier 1 methodology assumes that the soil is removed and disposed of under aerobic conditions where the C
stock is emitted as CO2 (oxidised) during the year of the extraction. The C stock is taken as all soil carbon except
any refractory (unoxidisable) carbon. In mangrove soils 4% of the C stock is refractory (Annex 4A.4) and this istaken to be representative of the refractory carbon in tidal marshes and seagrass meadows as well. Therefore,
after the initial conversion of the soil pool in the year in which the activity occurs, CO2 emissions are reported aszero. It is good practice to track these lands to consider management activities that may occur on those lands inthe future and for higher tier estimations. The choice of method follows that in Section 4.2.2. For Tier 1, CO2
emissions are reported as the conversion in soil C where this activity occurs and the type of vegetation and theavailability of activity data to distinguish between organic and mineral soils, determines which data is applied
from Table 4.11.

4.24

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

870
871
872
873
874
875
876
877

Tier 2

878
879
880
881
882

Tier 3Tier 3 methods can employ models to estimate CO2 emissions based on the effect of temperature and salinity on
soil oxidation both seasonally and with climate and vegetation type. At Tier 3 it is good practice for countries tovalidate models with field measurements. Tier 3 methods may also include site specific measurements of e.g. Ccontent, BD, clay content, salinity, redox etc. to determine the underlying processes of emissions.

883

CHOICE OF EMISSION FACTORS - ORGANIC AND MINERAL SOILS

884
885
886
887
888

Tier 1

At Tier 2, methodology can be applied to disaggregate by vegetation type and soil type. For the specificextraction activity, countries may use national data to determine their particular extraction processes and thevolume of soil removed, if sufficient data are available. Because tidal marshes can occur in a range of climates,
disaggregating by climate may also be applied to improve estimates if those country-specific data are available.
Tier 2 may also define the area of the aquaculture and salt production activity to refine the estimate for the soil C
stock that is excavated to construct the pond, including specific information on the depth of pond excavatedduring the construction phase.

Default Tier 1 emission factors for extraction in organic and mineral soils, are given in Table 4.11 for the threemajor vegetation types in coastal wetlands. These values are to be used in conjunction with Equation 4.6 toestimate emissions. If soil type is not known, a generic default value for aggregated organic and mineral soils
can be applied (Table 4.11).

889
TABLE 4.11 SOIL C STOCKS FOR MANGROVE, TIDAL MARSH AND SEAGRASS MEADOWS FOR
EXTRACTION ACTIVITIESORGANIC SOILS (TONNES C HA-1)
Vegetation type
MangroveTidal marsh

SOBEFORE
471

2

340

3

95% CI1

range

n436, 510

216 – 935

43

315, 366

221 – 579

35

NA4

Seagrass meadow

MINERAL SOILS (TONNES C HA-1)
Vegetation type

95% CI1

rangen

Mangrove286

5247, 330

55 - 1376

77

Tidal marshes

2266

202, 252

15.6 – 623

82

784,1399.1 – 829

89

Seagrass meadow8

SOBEFORE

108

AGGREGATED ORGANIC AND MINERAL SOILS (TONNES C HA-1)
SOBEFORE

95% CI1

Mangrove386

Tidal marsh

255

Vegetation type

range

n

351,42455 - 1376

119

254,297

15.6-623

117

1

95%CI of the geometric mean
Adame et al. (2012), Breithaupt et al. 2012, Chmura et al. 2003, Donato et al. 2011, Kauffman et al.
2011, Osborne et al. 2011, Vegas-Vilarrúbia et al. 2010 .
3
Anisfeld et al. 1999, Callaway et al. 1996, Callaway et al. 2012, Chmura & Hung 2004, Craft et al.
1988, Craft 2007, Hussein et al. 2004, Kearney & Stevenson 1991, Orson et al. 1998, Markewich et al.
1998, McCaffrey & Thomson 1980.
4
Seagrass meadows assumed to be on mineral soils.
2

5

Donato et al. 2011, Chmura et al. 2003, Breithaupt et al. 2012, Fujimoto et al. 1999, Adame et al.
2012, Perry & Mendelssohn 2009, Ren et al. 2010, Kauffman et al. 2011, Ray et al. 2011, Zhang et al.
2012, Khan et al. 2007, Matsui 1998.
6
Cahoon et al. 1996, Callaway et al. 2012, Chmura & Hung 2004, Connor et al. 2001, Craft et al.
1988, Craft 2007, Hatton 1981, Kearney & Stevenson 1991, Livesley & Andrusiak 2012, Loomis &DRAFT 2013 Wetlands Supplement

4.25

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
Craft 2010, Morris & Jensen 2003, Oenema & DeLaune 1988, Patrick & DeLaune 1990, Roman et al.
1997, Yu & Chmura 2009.
78

Fourqurean et al 2012
For Extraction only

890
891
892
893
894

Tier 2

895
896
897
898
899
900

Tier 3
A Tier 3 approach could use models that take into account the time-dependent nature of the CO2 fluxes over arange of timescales. For example, during the construction phase a pulse of CO2 efflux from soil directly after
mangrove clearing and prior to excavation, followed by a logarithmic decline in CO2 fluxes over time has beenshown to occur (Lovelock et al., 2011). For fish and shrimp ponds, the actual area excavated and the depth towhich soil is excavated, could be taken into account as this varies with aquaculture and salt production practices.

901

CHOICE OF ACTIVITY DATA

902
903

Choice of activity data follows from guidance above provided in Section 4.2.2.1 as the area in which theextraction activities occur will be the same area applied for each C pool.

904

UNCERTAINTY ASSESSMENT

905
906
907
908
909
910
911
912
913

Variability in soil C stocks will derive from a number of sources. The soil stock represents global averages andmay therefore under or over-estimate emissions and removals when applied to specific countries. Deriving
country-specific C stocks can reduce uncertainties using Tier 2 methodology. There may also be significant
within country differences due to: (1) the dominant species present in mangrove, tidal marsh or seagrassmeadows, (2) climatic conditions and (3) general environmental setting in which the vegetation is found, all ofwhich may influence the C stock. When deriving global emission factors, uncertainties can also be introduced byareas where there is greater prevalence of data from specific regions of the globe. The change in C stock on
extraction is dependent on the value assigned to the percent refractory organic carbon. The value applied is takenfrom soil in mangrove and may not be fully representative of the value for tidal marsh and seagrass meadows.

914
915

Tier 2 includes the use of country specific emission factors that can be applied to disaggregate by soil type andvegetation type to improve on Tier 1 estimates that were calculated using a generic default value. Countryspecific data may include incorporation of excavation depth to improve estimation of soil extracted.

4.2.3 Rewetting and revegetation of mangroves, tidal marshes
and seagrass meadows

916
917
918
919
920
921
922
923
924
925
926

This section addresses the C stock changes and CO2 emissions and removals for the rewetting and revegetation
activities relating to mangroves, tidal marshes and seagrass meadows.

927

4.2.3.1

928
929
930
931
932
933

The initiation of soil C accumulation is only possible with the presence of vegetation, which is introduced bypurposeful seeding/planting or natural recolonisation For mangroves, methodological guidance for estimating
carbon stock changes in the biomass pool, including choice of method and choice of emission and removal
factors, follows Section 4.2.1.1 of this Chapter. For tidal marshes and seagrasses, changes in biomass carbon
stocks, are reported only for Tier 2 or higher estimations. Guidance for estimating biomass C stock changes fortidal marshes and seagrass meadows follow those presented in Volume 4, Section 6.2.1.1 of the 2006 IPCC

The rewetting and revegetation activity refers collectively to the following (1) rewetting, which saturates the soilof drained sites previously colonised by mangrove and tidal marshes and is a prerequisite for, and thus facilitates,
reestablishment of the original vegetation by natural recolonisation, direct seeding and/or purposeful planting, (2)raising or lowering the soil elevation to facilitate reestablishment of the original vegetation by natural
recolonisation, direct seeding and/or purposeful planting, (3) creation of coastal wetlands where they cannot be
verified to have previously occurred but probably did so given the proximity of the land to the coastal margin,and (4) reestablishment of seagrass on undrained soils by natural recolonisation, direct seeding and/or purposefulplanting.

4.26

BIOMASSDRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

934
935
936
937

Guidelines (Grassland Remaining Grassland) for Gain-Loss and Stock-Difference methods. These are used withcountry-specific data on aboveground biomass stocks and aboveground-belowground (R) ratio provided inTables 4.9 and 4.10. Refer to Volume 4, Section 6.2.1.4 of the 2006 IPCC Guidelines for calculation steps useful
in applying these methods.

938

4.2.3.2

939
940
941
942
943
944
945

For mangroves, methodological guidance for estimating carbon stock changes in the DOM pool, including
choice of method and choice of emission and removal factors, follows Section 4.2.1.2 of this Chapter. For tidal
marshes and seagrasses, changes in biomass carbon stocks, are reported only for Tier 2 or higher estimations.
Guidance for estimating DOM C stock changes for tidal marshes and seagrass meadows follow those presented
in Volume 4, Section 6.2.2.1 of the 2006 IPCC Guidelines (Grassland Remaining Grassland) for Gain-Loss andStock-Difference methods. These are used with country-specific data. Refer to Volume 4, Section 6.2.2.4 of the2006 IPCC Guidelines for calculation steps useful in applying these methods.

946

4.2.3.3

947
948
949
950
951
952
953
954
955

The guidance provided in this section on soils differs from that in Chapter 3 (this supplement) because, on
coastal wetland soils, revegetation as part of rewetting leads to the accumulation of soil organic carbon whenvegetation is reestablished and a CO2 sink is then developed. The CO2 emission factor is approximated as zero
when resaturated soils are devoid of vegetation. This is consistent with the default EFs for rewetted organic soils
presented in Chapter 3 of this supplement. Based on information for natural fluxes from rewetted organic soils, it
is consistent with data illustrating that rewetting effectively stops soil organic matter oxidation but does notnecessarily reestablish the soil C sink function
Guidance for inventories of rewetting and revegetation activities of coastal wetlands follows the assumptions at
Tier 1 level of estimation that:

956
957
958

i. upon rewetting and revegetation of previously drained soil, creation of a mangrove or tidal marsh or on
restablishment of a seagrass meadow, soil C accumulation is initiated when natural vegetation becomes
established.

959

ii. the rate of soil C accumulation is instantaneously equivalent to that in natural settings.

DEAD ORGANIC MATTERSOIL CARBON

960
961
962
963
964
965
966
967
968
969

Craft et al., (2003) found that (a) soil C accumulation, developed almost instantaneously with the establishmentof vegetation along a chronosequence of 1- to 28-yr old constructed marshes and (b) a similar soil C
accumulation rate over 10 years in a natural and created marsh (Craft et al., 2002) and over 20 years in a createdmangrove (Osland et al., 2012). Given this equivalence, estimates of soil carbon accumulation rates inmangroves, tidal marshes and seagrass meadows (Chmura et al., 2003, Breithaupt et al., 2012, Duarte et al., 2012)
make it possible to quantify C gains at sites characterised by rewetting and revegetation activities. A transition
time for soil C stocks to become equivalent to those in natural/undrained settings with vegetation will exceed theland-use transition time of the typically used land-use category conversions (i.e. 20 years). It is suggested toapply the EF for soil C accumulation until such time as stocks are equivalent to soil C stocks innatural/undrained settings with vegetation based on available data and expert judgement.

970

CHOICE OF METHOD

971
972

Changes in soil carbon resulting from rewetting and revegetation activities for mangroves, tidal marshes andseagrasses are estimated because they represent potentially large C removals from the atmosphere.

973
974
975

Tier 1
At Tier 1, the default method, EFREWET values are to be used in conjunction with Equation 4.7 to estimate CO2
emissions.

976

DRAFT 2013 Wetlands Supplement

4.27

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
977
978
979
980

EQUATION 4.7
CO2 EMISSIONS ON REWETTED COASTAL WETLANDS
CO2SO-REWET = ∑,v,s,c(AREWET * EFREWET)v,s,cwhere,

981
982

CO2SO-REWET = CO2 emissions associated with rewetting and revegetation activities by vegetation type (v),soil type(s) and climate (c); tonnes C yr-1

983
984

AREWET1 = Area of soil that has been influenced by rewetting and revegetation activities by vegetation
type (v), soil type(s) and climate (c); ha

985
986

EFREWET1 = CO2 emissions from aggregated mineral and organic soils that have been influenced byrewetting and revegetation activities by vegetation type (v), soil type(s) and climate (c); tonnes C ha-1 yr-1

987
988

1

989
990
991
992
993
994
995

At Tier 1, EFREWET is applied (Table 4.14) when vegetation has been established through replanting or reseeding.
If, however, re-establishment of vegetation is expected to occur by recolonization, a Tier 1EFREWET = 0 is applied.
It is good practice to document the basis on which the EFREWET is applied. When vegetation has been establishedthe EFREWET is disaggregated with respect to vegetation type. Organic and mineral soils are not differentiated atTier 1 within any particular vegetation type, as the organic C inputs mainly derive from the production ofaboveground and belowground biomass under similar conditions of soil saturation. Land area estimates should
be based on land classification within the new land category (if applicable) to apply Tier 1 EFREWET.

996
997
998
999

Tier 2

1000

Tier 3

1001
1002
1003
1004
1005
1006

Under the Tier 3 method, the land use prior to rewetting, its climate and vegetation type could be taken into
account. A comprehensive understanding and representation of the dynamics of CO2 gas emission factors, basedon field measurements (such as C-content, BD, clay content, salinity, redox) could be employed at Tier 3. A Tier
3 approach could also use empirical measurements and models that take into account the time-dependent natureof the CO2 fluxes over a range of timescales (Morris et al., 2012), location relative to the low to high intertidal
zone (Alongi 2010) or other dynamics (Craft 2001).

1007

CHOICE OF EMISSION/REMOVAL FACTORS

1008

Tier 1

1009
1010
1011
1012
1013
1014
1015
1016
1017
1018

The choice of EFs at Tier 1 is applied based on the coastal wetland vegetation type being established through therewetting and revegetation activity. It is assumed that within each vegetation type, CO2 emissions are the same
regardless of how the suitable conditions for revegetation are facilitated. If vegetation is reestablished throughdirect reseeding or purposeful planting, apply EFREWET in Table 4.12. If the rewetting and revegetation activity is
associated with recolonization (no direct replanting or reseeding), apply EFREWET = 0. It is good practice toevaluate and document these activities (See Choice of Activity Data below) and modify what EF is applied, as
appropriate. If the rewetting and revegetation activity results in patchy or patchies of biomass (if coverage data
are available), EFREWET >0 should only be applied when the mangrove, tidal marsh plant or seagrass canopy
covers at least 10% of the overall area. This consideration follows the definition of forest (Table 4.2, Chapter 4,
Volume 4, 2006 IPCC Guidelines).

EFREWET = 0 for rewetted and naturally saturated soils where no vegetation has been re-estabished or where reestablishment is expected to occur by recolonization.

Under the Tier 2 method, country specific C accumulation rates could be dissagregated with respect to area oforganic and mineral soils. Where such country-specific data can be acquired and used to improve estimations,disaggregation by climate zone could also be applied.

1019
1020
1021
1022
1023

4.28

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1024
1025
TABLE 4.12 ANNUAL EMISSION FACTORS (EF) ASSOCIATED WITH REWETTING (EFREWET) ON-1-1
AGGREGATED ORGANIC AND MINERAL SOILS (TONNES C HA YR ) AT INITIATION OF VEGETATIONREESTABLISHMENT
Ecosystem
MangroveTidal marsh
Seagrass meadow

EFREWET

95% CI4

range

n

2.0

0.10 – 10.2

69

0.7

1.1

0.05 – 4.65

66

0.2

0.7

0.09 – 1.12

6

-1.62

1

1.3

-0.91

2

-0.43

3

1

Breithaupt et al. 2012, Chmura et al. 2003, Fujimoto et al. 1999, Ren et al. 2010.
2
Anisfeld et al 1999, Cahoon et al. 1996, Callaway et al 1996, Callaway et al 1997, Callaway et al 1998,
Callaway et al 1999, Callaway et al. 2012, Chmura & Hung 2003, Hatton 1981, Craft 2007, Kearney &Stevenson 1991, Markewich et al. 1998, Oenema & DeLaune 1988, Orson et al 1998, Patrick & DeLaune 1990,
Roman et al 1997.
3
Mateo & Romero 1997, Serrano et al. 2012
4
95% CI of the geometric mean

1026
1027

Tier 2

1028
1029
1030
1031

In a Tier 2 approach, country-specific emission factors for the rewetting and revegetation activities could beapplied and the assumption of EFREWET=0 in areas where vegetation had not been established could also bereassessed. Country-specific emission factors could be applied based on disaggregation of organic and mineralsoils and climate.

1032

Tier 3

1033
1034
1035
1036
1037
1038
1039
1040
1041

In a Tier 3 approach, field measurement of soil organic carbon content and CO2 emissions from areas whererewetting and revegetation activities occur could be used to develop an empirical relationship (for example, asimple regression equation) that can be used across other sites where rewetting and revegetation activities occur
within a particular area or country. Country-specific values can be developed to model possible time-dependentchanges in CO2 emissions. Soil C accumulation rates will likely change, as vegetation grows and biomass
matures. Increased inundation and soil saturation, as a result of intertidal location in tidal marshes andmangroves, will accelerate development of soil characteristics of revegetated soils. Thus, rates of CO2 emissions
in these tidal wetlands will vary in relation to a combination of these factors and consideration of them would
result in more accurate estimation of CO2 emissions.

1042

CHOICE OF ACTIVITY DATA

1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058

Historical photos and coastal wetland maps, if available at the appropriate spatial resolution, may be used toestimate the pre-restored wetland area. Information on regional wetland restoration and creation projects
worldwide can be obtained from the Global Gateway to Geographic Information Systems of the FAO(www.fao.org) as well as from the websites, www.wetlands.org and www.globalrestorationnetwork.org. Withina given country, government agencies responsible for issuance of permits for restoration/creation/alteration of
wetland are to be consulted for information of area data on the wetlands being considered. In addition, many
countries may have a process for reporting rewetting and revegetation activities as permission is often required.
For example, in Australia, the Environmental Protection Agency in Western Australia approves revegetation
projects as part of their Ministerial Conditions. The Australian Government Department of Sustainability,
Environment, Water, Population and Communities also directs the Federal Minister to approve or reject
revegetation programs. The establishment of vegetation and/or change in areal extent can be reviewed on a five
year period and assessed for accurate implementation of the appropriate soil EF. If data are lacking, expert
judgement about success rates of projects implemented under similar conditions could be used for intitalassessments (examples are size of project, vegetation type, tidal range, proximity to coast, climate). In general,
for rewetting activities that include purposeful planting or direct reseeding, an EFREWET (using Table 4.12) isappropriate for Tier 1 estimation. Information on which the choice in EF is based should be documented.

1059

UNCERTAINTY ASSESSMENT

1060
1061

Uncertainties in estimating CO2 emissions and removals from rewetting and revegetation of mangroves, tidal
marshes and seagrass meadows largely lie in the underlying assumptions and area to which the EFs are applied.

DRAFT 2013 Wetlands Supplement

4.29

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
1062
1063
1064
1065
1066

The EFREWET in Table 4.12 represent global averages and have large uncertainties associated with their value due
to variability in soil C accumulation rate with 1) depth of the intertidal zone, 2) the dominant species type, its
morphology and rate of growth, 3) climate. The underlying assumption of EFREWET=0 for rewetted/saturated soils
where vegetation has not been re-established may introduce uncertainty into estimates. Also, the assumption ofcomplete areas with or without vegetation cover could introduce under- or overestimates.

1067

4.2.4 Drainage in mangroves and tidal marshes

1068
1069
1070
1071
1072
1073
1074

This section addresses the changes in C stock and CO2 emissions and removals for drainage in mangroves andtidal marshes. Drainage may be accompanied by land clearing, also resulting in changes in biomass and DOM
pools. If burning accompanies drainage, it is good practice to report emissions from changes in those C pools.
For methods to estimate changes in carbon stock in biomass, and for default data, refer to Section 4.2.1 of thisreport for guidance on mangroves and Section 4.2.2 for guidance on tidal marshes. It is important to retain
information about land-use category conversion so that guidance in this supplement can be applied if a reversalof drainage conditions occurs.

1075
1076
1077
1078
1079
1080
1081

Drainage causes soils to dry and ordinarily increases rates of organic matter decomposition, resulting in loss ofsoil carbon via CO2 release (Armentano and Menges 1986). This response varies with climate (Pozo and Colino
1992) and locally with soil salinity and texture, and the quantity of labile organic matter available (Heminga etal., 1998; Setia et al., 2011). Activities associated with extensive lowering of the water table are often linked tothe construction of drainage channels leading to CO2 fluxes due to oxidation of DOC and POC in the water
carried by drainage channels. However, there is currently not enough information to provide emission factors for
DOC and POC export (see Appendix 4a.1 on Future methodological development).

1082

4.2.4.1

1083
1084
1085

Methodological guidance for estimating carbon stock changes in the biomass pool, including choice of method
and choice of emission and removal factors, follows Section 4.2.3.1 of this Chapter. For tidal marshes, increasein biomass stocks in a single year is assumed equal to biomass losses from mortality in that same year at Tier 1.

1086

4.2.4.2

1087
1088
1089

Methodological guidance for estimating carbon stock changes in the DOM pool, including choice of method andchoice of emission and removal factors, follows Section 4.2.3.2 of this Chapter. For tidal marshes, the CO2
emissions and removals from change in biomass and DOM pools is reported as zero at Tier 1.

1090

4.2.4.3

1091
1092
1093
1094
1095

Annual C losses from drained mineral and organic soils are applied similarly for mangroves and tidal marshes atTier 1 level of estimation (Table 4.14). Data on CO2 emissions from drainage in mangroves is limited, however,
the CO2 emission rate from drainage in tidal marshes was considered to provide an appropriate Tier 1 default
emission factor. This value is also consistent with drained forest default EF presented in Chapter 2 of this
supplement.

1096

CHOICE OF METHOD

1097
1098
1099

Tier 1

BIOMASS

DEAD ORGANIC MATTERSOIL CARBON

Guidance for inventories on drainage in coastal wetlands follows the assumptions at Tier 1 level of estimation
that:

1100

i.

emissions persist as long as the soil remains drained or as long as organic matter remains

1101
1102
1103

ii.

the drainage condition is characterized by full drainage (i.e. the water table has been changed to 1 m
below the soil surface for organic and mineral soils), consistent with the Tier 1 approach in Chapter 2,
this supplement.

4.30

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1104
1105

Emissions from drained coastal wetland soils are estimated at Tier 1 for mangrove forests and tidal marshes are
estimated using Equation 4.8.

1106
1107
1108

EQUATION 4.8
CO2 EMISSIONS ON DRAINED ORGANIC AND MINERAL SOILS
CO2-SO-DR = (ADR • EFDR)

1109

where:

1110
1111

CO2-SO-DR = CO2 emissions from aggregated organic and mineral soil C associated with drainage;
tonnes C yr-1

1112

ADR = land area under drainage; ha1113

EFDR, = CO2 emissions from organic or mineral soil C associated with drainage; tonnes C ha-1 yr-1

1114
1115
1116

Tier 2The Tier 2 estimation method is the same as the Tier 1 method, but national data can be used to additionallydisaggregate by vegetation, soil type and regional climatic factors, if such data are available at reasonable cost.

1117
1118
1119
1120
1121
1122

Tier 3 methods could take account of differences in the management of the drained wetland. Empiricalmeasurements of gas flux based on site specific measurements of e.g. C-content, BD, clay content, salinity,
redox etc. to determine the underlying processes of emissions could be included. Site differences in frequency ofdrainage activity could also be considered at Tier 3 methods. Other factors that could be used to apply
disaggregated data include salinity and tidal export of DOC and POC (Appendix 4a.1).

1123

CHOICE OF EMISSION/REMOVAL FACTORS

1124

Tier 11125
1126

At Tier 1, a generic default emission factor is applied for drainage, regardless of vegetation or soil type (Table
4.13). That is, the same EF is applied regardless of the management activity involving soil drainage.

Tier 31127
TABLE 4.13 ANNUAL EMISSION FACTORS (EFDR) ASSOCIATED DRAINAGE (EFDR) ON AGGREGATED ORGANIC AND MINERAL-1-1
SOILS (TONNES C HA YR )Ecosystem
Tidal marshes and mangroves
1

EFDR
7.9

1

95% CI

Range

N

5.2, 11.8

1.2 – 43.9

22

Camporese et al. (2008), Deverel & Leighton (2010), Hatala et al. (2012), Howe et al. (2009), Rojstaczer & Deverel (1993)

295%CI of the geometric mean

1128
1129

Tier 21130
1131

Tier 2 emission factors apply country-specific data disaggregated by soil type, vegetation type, and climate,
where feasible.

1132

Tier 3

1133
1134
1135
1136
1137
1138
1139
1140

In a Tier 3 approach, field measurements of soil organic carbon content and CO2 emissions from the drained sitewould be useful to develop an empirical relationship (for example, a simple regression equation of soil carbon
content versus rate of carbon removal) that can be used across other drained sites within a particular area orcountry. Country-specific values can thus be developed to model possible time-dependent changes in CO2emissions such as changes in relation to timing and rate of soil drainage, depth of drainage and additional
national information about mean annual water table and land-use type or intensity. A comprehensive
understanding and representation of the dynamics of CO2 gas emission factors, based on field measurements(such as C-content, BD, clay content, salinity, redox) could be employed at Tier 3.

DRAFT 2013 Wetlands Supplement

4.31

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
1141

CHOICE OF ACTIVITY DATA

1142
1143
1144
1145
1146

Tier 1The Tier 1 approach requires area data of drained land for each land-use category that have been identified incoastal wetlands. Classification systems for activity data that form the basis for a Tier 1 inventory are provided
in the respective land-use Chapters of the 2006 IPCC Guidelines. For coastal wetlands, the predominant land-use
category conversion is to Cropland and Grassland.

1147
1148
1149
1150
1151
1152
1153

Activity data for higher tier estimates are generally derived following the methods presented in Chapter 3 of the2006 IPCC Guidelines. To disaggregate by soil type and vegetation type, several institutions, including ISRIC
and
FAO
have
country-specificandglobalmaps
thatincludeorganicsoils(http://www.fao.org/geonetwork/srv/en/main.home or http://www.isric.org/). A global consortium has been
formed to make a new digital soil map of the world at fine resolution (http://www.globalsoilmap.net/). Other
activity data for

1154
1155
1156

Drainage is assumed to result in persistent emissions from soils as long as the management system remains inplace. Activity data may be spatially explicit and could be disaggregated by type of management, if appropriate
emissions factors are available.

1157
1158

The combination of land-use databases and soil maps or spatially explicit data allow delineation of combinationsof land-use categories, climate domains, and management systems and their changes over time on organic soils.

1159

Information sources about drainage with adequate disaggregation may include:

1160
1161



National land-use statistics, land-use maps and soil maps, maps of water and nature conservation zones withrestrictions for water management, wetlands.

1162
1163
1164
1165
1166



National water management statistics: in most countries, the agricultural land base including Croplands is
usually surveyed regularly, providing data on distribution of different land-uses and other aspects ofmanagement, often at sub-national regional level. These statistics may originate, in part, from remotesensing methods, from which additional information about wetness or periods with seasonal flooding could
be extracted.

1167
1168
1169
1170
1171



Inventory data from a statistically based, plot-sampling system of water table wells, ditches and surface
waters on organic soils: water table is monitored at specific permanent sample plots either continuously oron plots that are revisited on a regular basis. It has to be documented that the water data represent the watertable in the organic soil and for what land-use and drainage stratum and that the data cover a representative
period, which represents a multi-year mean annual water table.

1172



Water management plans and documentation from water management installations.

1173



Drainage maps.

1174

UNCERTAINTY ASSESSMENT

1175
1176
1177
1178
1179
1180
1181

Three broad sources of uncertainty exist in estimating emissions and removals from drainage: 1) uncertainties inland-use and environmental data; 2) uncertainties in the emission/removal factors for Tier 1 or 2 approaches; and3) model structure/parameter error for Tier 3 model-based approaches, or measurement error/sampling
variability associated with Tier 3 measurement-based inventories. In general, precision of an inventory is
increased and confidence ranges are smaller with more sampling to estimate values for these categories, while
accuracy is more likely to be increased through implementation of higher Tier methods that incorporate countryspecific information.

1182
1183
1184

For Tier 1, the default uncertainty level of emissions/removal factors is the 95% confidence interval in Table
4.13. Countries developing specific emission factors for their inventories at higher tiers should assess theuncertainty of these factors.

1185
1186
1187
1188
1189

If using aggregate land-use area statistics for activity data (e.g., FAO data), the inventory agency may have toapply a default level of uncertainty for the land area estimates, for example. It is good practice for the inventory
compiler to derive uncertainties from country-specific activity data instead of using a default level of
uncertainty. Uncertainties in activity data may be reduced through a better national system, such as developing
or extending a ground-based survey with additional sample locations and/or incorporating remote sensing toTier 2 and 3

4.32

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1190
1191
1192

provide additional coverage. Uncertainties in activity data and emission/removal factors need to be combinedusing an appropriate method, such as simple error propagation equations. Details are given in Chapter 3, Volume
1 of the 2006 IPCC Guidelines and in Chapter 5 of the GPG-LULUCF.

1193

4.3 NON-CO 2 EMISSIONS

1194
1195

This section provides methods for estimating the emissions of CH4 emissions from rewetted mangroves and tidal
marshes and N2O from aquaculture.

1197

4.3.1 CH 4 emissions from rewetted soils of mangrove andtidal marsh

1198
1199
1200
1201
1202
1203
1204

Rewetting of drained soils, through reconnection of hydrology, shifts microbial decomposition from aerobic toanaerobic conditions, increasing the potential for CH4 emissions (Harris et al 2010). In environments where low
salinity also occurs (especially <5 ppt), microbial decomposition of organic matter may result in production of
CH4. However, in soils saturated with seawater, microbial reduction of sulfate to sulfide will generally occur
before methanogens produce CH4. A strong inverse relationship between CH4 emissions and salinity ofmangrove soils exists (Purvaja & Ramesh, 2001). A review by Poffenbarger et al. (2011) showed that CH4
emissions decrease as salinity in tidal marshes increases.

1205
1206
1207

Guidance for estimating CH4 emissions associated with rewetting land previously characterised by mangrove
and tidal marsh vegetation differs from that for estimation of CO2 emissions in that, at Tier 1 level of estimation,
the EF remains the same for CH4, regardless of extant vegetation.

1208

4.3.1.1 C HOICE OF METHOD

1209
1210
1211

In the case of rewetting of lands that had been previously been in agricultural (or any other drained) land-use
category, the Tier 1 method estimates CH4 emissions without considering the land-use prior to rewetting.

1196

Tier 1

1212
1213
1214
1215
1216

EQUATION 4.9

CH4 EMISSIONS IN REWETTED TIDAL MARSHES AND MANGROVES
CH4SO-REWET = ∑v(Arewet * EFrewet)v
where,

1217
1218

CH4SO-REWET = CH4 emissions associated with rewetted and created coastal wetlands by vegetation type (v)kg CH4 yr-1

1219
1220

AREWET = Area of soil that has been rewetted (including tidal marsh or mangrove wetland creation), byvegetation type (v); ha

1221
1222

EFrewet = CH4 emissions from mineral and organic soils that have been rewetted by vegetation type (v); kg
CH4 ha-1 yr-1

1223
1224
1225
1226

At Tier 2, country-specific data can be applied. Improved estimates can be produced if country-specific data
could include more disaggregation by salinity and vegetation type.

1227
1228
1229
1230
1231

At Tier 3, country-specific values can be used and developed to model possible time-dependent changes in CH4emissions. Tier 3 methods may also consider vegetation composition and density, as plants can act as a conduit
for gas exchange between the soil and atmosphere (e.g. Burdick 1989, Purrvaja and Ramesh 2001, Kristensen etal., 2008).

Tier 2Tier 3

DRAFT 2013 Wetlands Supplement

4.33

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
1232

4.3.1.2 C HOICE OF EMISSION FACTORS

1233

Tier 1

1234
1235
1236
1237
1238
1239
1240
1241

The Tier 1 CH4, emission factors are found in Table 4.14 and should be used in conjunction with Equation 4.9 toestimate emissions taking into account vegetation type (and associated salinity level). The choice of emission
factor at Tier 1 is based on the difference between rewetting by freshwater and brackish/saline waters (Annex4.1), with rates of CH4 emissions approximating 0 in salt marshes and mangroves but greater than zero in
freshwater tidal marshes (Table 4.14). For rewetting that results in salinities in the 5-18 ppt range as may be
typical for rewetting of brackish marsh (i.e. neither tidal freshwater or tidal salt water/mangrove), the Tier 1
assumption is to apply an annual CH4 emission rate = 0. Within each vegetation type, CH4 emissions are thesame regardless of the management activity involving rewetting at Tier 1.

1242
TABLE 4.14 EMISSION FACTORS FOR CH4 FOR TIER 1 ESTIMATION OF REWETTED LAND PREVIOUSLY VEGETATED BY TIDALMARSHES AND MANGROVES

Vegetation Type

Salinity(ppt)

Tidal freshwater marsh
Tidal brackish marsh2 and tidal salt marsh andmangrove

0.5-5
5-40

EFrewet(kg CH4 ha-1 y-1)

EFrewet Range(kg CH4 ha-1 y-1)

01

0 - 4011201

30 - 4040

Error(95%CI)
±80%
±90%

1

Poffenbarger et al. 2011
Brackish marshes with salinities in the 5-18ppt approximate an order of magnitude lower rates than from tidal freshwater marsh (as defined
here 0.5-5ppt), so a tier 1 assumption is to apply 0
2

1243
1244

Tier 2

1245
1246
1247

In a Tier 2 approach, country-specific CH4 emissions should be used and will provide better estimates based on
the salinity of water used to rewet the mangrove or tidal marsh, particularly to determine CH4 emissions fromtidal brackish marshes.

1248

Tier 3

1249
1250
1251
1252
1253
1254

In a Tier 3 approach, field measurements of soil salinity and CH4 emissions from the rewetted site could be used
to develop an empirical relationship (for example, a simple regression equation of salinity versus rate of methane
emission) and applied across other rewetted sites within a particular area or country. Country-specific values canthus be developed to model possible time-dependent changes in CH4 emissions such as changes in relation tofrequency of tidal inundation, frequency of the rewetting activity and elevation from the water’s edge. Suchconsiderations would result in more accurate estimation of CH4 emissions.

1255

4.3.1.3

1256
1257
1258
1259
1260
1261
1262
1263
1264
1265

To estimate emissions using CH4 emission factors refer, in part, to the guidance for rewetting in section 4.2.3
above. The EF should be applied to the specific type of vegetation that will be reestablished, which is associatedwith salinity. When salinity data are not available the type and location of rewetting may be used as a proxy forsalinity. For example, breaching of sea walls and rewetting in an estuarine setting will result in rewetting withsaline or brackish waters. If rewetting occurs in a river upstream of an estuary in a location still affected by tidal
movements, rewetting with water of salinity <5ppt is likely. When applying guidance for tidal freshwater marsh,
it is good practice to determine the inland boundary for rewetting of tidal freshwater wetlands as based onnational circumstances, and to consistently apply these conditions to identifying these rewetted lands. If more
information is available on salinity concentrations associated with the area being rewetted, better estimates ofCH4 emissions can be determined. Information used for these assessments should be documented.

1266

4.3.1.4 U NCERTAINTY ASSESSMENT

1267
1268

There have been few empirical measurements upon which to base emission factors disaggregated by factors suchas temperature, tidal frequency or duration of inundation which introduce uncertainty in global default emission

4.34

CHOICE OF ACTIVITY DATADRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1269
1270
1271
1272
1273

factors. However, higher tier approaches can take these factors into account to improve estimations. Few reports
are available to give specifics of the types of rewetting activities that may vary geographically. Because activity
data may be limited in terms of delineating salinity boundaries to apply more constrained CH4 emission factors,
aggregation of data to produce Tier 1 emission factors was based upon expert knowledge. There is also
uncertainty in the time, depth of soil affected, and the contribution of vegetation to rate of CH4 loss.

1275

4.3.2 N2 O emissions during aquaculture use in mangroves,tidal marshes and seagrass meadows

1276
1277
1278
1279
1280
1281
1282
1283
1284
1285

The most significant activity contributing to N2O emissions from managed coastal wetlands is aquaculture. Onethird of global anthropogenic N2O emissions are from aquatic ecosystems, and nearly 6% of anthropogenic
N2O−N emission is anticipated to result from aquaculture by 2030 at its current annual rate of growth (Hu et al.,2012). Shrimp and fish cultivation increases nutrient loads in culture ponds. As opposed to indirect N2Oemissions originating from activities on terrestrial lands or as wastewater treatment, coastal wetland aquaculture
occurs as a direct source of N2O from coastal wetlands, including mangroves and tidal marshes from aquaculture
pond use. In seagrass meadows, this direct N2O source arises from N added to fish cages (eg. off-shore
installations). While this differentiation should assure no double-counting, it is good practice to evaluate thisassessment considering national circumstances. As such, this new activity fills a gap in the current reporting on
direct and indirect sources of N2O emissions.

1286
1287
1288

N2O is emitted as a by-product of the conversion of ammonia (contained in fish urea) to nitrate throughnitrification and nitrate to N2 gas through denitrification (Hu et al., 2012). N2O emissions can readily be
estimated from fish production data.

1289

4.3.2.1

1290

TIER 1

1291
1292
1293

N2O emissions from aquaculture ponds can be estimated based on fish production of the aquaculture activity.
N2O emission estimation follows a modified form of Equation 11.1 from Chapter 11, Volume 4 of the 2006
IPCC Guidelines and is presented here in (Equation 4.10).

1274

CHOICE OF METHOD

1294
1295
1296
1297
1298
1299

EQUATION 4.10
DIRECT N2O EMISSIONS FROM AQUACULTURE USE
N2O-NAQ = FF * EFF (based on fish production)
where:

1300

N2O-NAQ = annual direct N2O-N emissions from aquaculture use, kg N2O-N yr-1

1301

FF = annual fish production, kg fish yr-1

1302

EFF = emission factor for N2O emissions from fish produced, kg N2O-N (kg fish produced)-1

1303

TIER 21304
1305

Tier 2 estimation methodology follows that of Tier 1 with the added information provided by country-specific
data.

1306

TIER 3

1307
1308
1309

Tier 3 estimation methodology could include the consideration of fish type, type of feed and stocking density,
category of aquaculture (fish species or feed stuff), aquaculture use intensity, and impact of environmental
factors e.g. climate zone, season, and salinity.

DRAFT 2013 Wetlands Supplement

4.35

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
1310

4.3.2.2 C HOICE OF EMISSION FACTORS

1311

TIER 1

1312
1313
1314
1315
1316

Hu et al. (2012) estimates that 1.8% of the nitrogen fed to aquaculture systems is emitted as N2O (0.00169 kg
N2O-N is emitted per kg fish produced). The EFF is applied during the, in use, phase of aquaculture (Table 4.15).
In the construction and discontinued phases, non-CO2 emissions are assumed negligible, EF=0. At Tier 1,
countries could consider applying this EF to other species groups under aquaculture production. Because the EF
is developed for fish, application may introduce additional uncertainty.

1317
Table 4.15 Emission factos (EFF) for N2Oemission from aquaculture in mangroves, tidal marshes andseagrass meadows
Default EF(kg N2O-N per kg fish produced)

Uncertainty Range

Reference

0.00169 kg N2O-N per kg fish produced

0.00163-0.00502

Hu et al. 2012

1318
1319

TIERS 2 AND 3

1320
1321
1322
1323
1324
1325

Under Tier 2 method, country specific emission factors for N2O are applied. At Tier 2, these country-specificemission factors could incorporate a different value for the proportion of N emitted as N2O as specified at Tier 1.
For Tier 3 emission factors, comprehensive understanding and representation of the dynamics based on direct
field measurements or models is involved, which estimates emission factors considering the category of
aquaculture (fish species or feed stuff), aquaculture use intensity, and impact of environmental factors e.g.
climate zone, season, and salinity.

1326

4.3.2.3

1327
1328
1329

Data
for
fish
production
areneeded.
These
data
can
be
obtainedfrom
FAO(http://www.fao.org/fishery/statistics/global-aquaculture-production/en). For additional guidance, see Section
4.2.1

1330

4.3.2.4 U NCERTAINTY ASSESSMENT

1331
1332
1333
1334
1335
1336

Emission factors for N2O emissions from aquaculture systems are based on protein content of fish, relationships
between total nitrogen content and wet weight of fish and the percent of nitrogen load emitted as N2O. As thesefactors can vary greatly, and in part on environmental conditions, high variation can occur among aquaculture
systems. Decreased uncertainty can be achieved at Tier 2 and 3 to reflect variability in N2O emissions based onfish species and type of food (pellets vs trash fish). Uncertainties in N2O emissions associated with stocking of
aquaculture facilities can be reduced greatly by better estimation of fish production.

CHOICE OF ACTIVITY DATA

1337
1338

References

1339
1340
1341
1342
1343
1344
1345
1346
1347

Abohassan, R. A. A., C. A. Okia, J. G. Agea, J. M. Kimondo, and M. M. McDonald. 2012. Perennial biomass
production in arid mangrove systems on the Red Sea coast of Saudi Arabia. Environmental Research Journal
6:22-31.
Adame, M.C., and C.E. Lovelock. 2011. Carbon and nutrient exchange of mangrove forests with the coastal
ocean. Hydrobiologia 663: 23-50.
Adame, M. F., R. Reef, J. A. Herrera-Silveira, and C. E. Lovelock. 2012. Sensitivity of dissolved organic carbon
exchange and sediment bacteria to water quality in mangrove forests. Hydrobiologia 691:239-253.
Adams, C. A., J. E. Andrews, and T. Jickells. 2012. Nitrous oxide and methane fluxes vs. carbon, nitrogen andphosphorous burial in new intertidal and saltmarsh sediments. Science of the Total Environment 434:240-251.

4.36

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401

Agostini, S., G. Pergent, and B. Marchand. 2003. Growth and primary production of Cymodocea nodosa in acoastal lagoon. Aquatic Botany 76:185-193.
Aioi, K. 1980. Seasonal change in the standing crop of eelgrass (Zostera marina L.) in Odawa Bay, central Japan.
Aquatic Botany 8:343-354.
Aioi, K., H. Mukai, I. Koike, M. Ohtsu, and A. Hattori. 1981. Growth and organic production of eelgrass(Zostera marina L.) in temperate waters of the Pacific coast of Japan. 2.Growth analysis in winter. Aquatic
Botany 10:175-182.
Aioi, K., and P. C. Pollard. 1993. Biomass, leaf growth and loss rate of the seagrass Syringodium isoetifolium on
Dravuni island, Fiji. Aquatic Botany 46:283-292.
Ajonina, G.M. 2008. Inventory and Modelling Mangrove Forest Stand Dynamics Following Different Levels ofWood Exploitation Pressures in the Douala-Edea Atlantic Coast of Cameroon, Central Africa. Ph.D.
Dissertation, Freiburg im Breisgau, Germany, 232 p.
Alberts, J. J., Z. Filip, M. T. Price, D. J. Williams, and M. C. Williams. 1988. Elemental composition, stable
isotope ratios and spectrophotometric properties of humic substances occurring in a salt marsh estuary. Organic
Geochemistry 12:455-467.
Allen, J. A., K. C. Ewel, B. D. Keeland, T. Tara, and T. J. Smith. 2000. Downed wood in Micronesian mangrove
forests. Wetlands 20:169-176.
Alongi, DM. 2010. The Energetics of Mangrove Forests. Springer Science.
Alongi, D. M., B. F. Clough, P. Dixon, and F. Tirendi. 2003. Nutrient partitioning and storage in arid-zone
forests of the mangroves Rhizophora stylosa and Avicennia marina. Trees-Structure and Function 17:51-60.
Alongi, D. M., A. D. McKinnon, R. Brinkman, L. A. Trott, M. C. Undu, Muawanah, and Rachmansyah. 2009.
The fate of organic matter derived from small-scale fish cage aquaculture in coastal waters of Sulawesi and
Sumatra, Indonesia. Aquaculture 295:60-75.
Amarasinghe, M. D., and S. Balasubramaniam. 1992. Net primary productivity of 2 mangrove forest stands on
the Northwestern coast of Sri-Lanka. Hydrobiologia 247:37-47.
Anisfeld, S. C., M. Tobin, and G. Benoit. 1999. Sedimentation rates in flow-restricted and restored salt marshes
in Long Island Sound. Estuaries 22:231-244.
Armentano, T. V. and Menges, E. S.: Patterns of change in the carbon balance of organic soil wetlands of thetemperate zone, Journal of Ecology 74:755–774.
Asmus, R. M., M. Sprung, and H. Asmus. 2000. Nutrient fluxes in intertidal communities of a South European
lagoon (Ria Formosa) - similarities and differences with a northern Wadden Sea bay (Sylt-Romo Bay).
Hydrobiologia 436:217-235.
Bandeira, S. O. 2002. Leaf production rates of Thalassodendron ciliatum from rocky and sandy habitats. Aquatic
Botany 72:13-24.
Birch, J. B., and J. L. Cooley. 1982. Production and standing crop patterns of giant cutgrass (Zizaniopsis
miliacea) in a fresh-water tidal marsh.Oecologia 52:230-235.
Blackburn, T. H., B. A. Lund, and M. D. Krom. 1988. C-mineralization and N-mineralization in the sediments ofearthen marine fishponds. Marine Ecology Progress Series 44:221-227.
Boon, P. I. 1986. Nitrogen pools in seagrass beds of Cymodocea serrulata and Zostera Capricornia of Moreton
Bay, Australia. Aquatic Botany 25:1-19.
Bouillon, S., A. V. Borges, E. Castaneda-Moya, K. Diele, T. Dittmar, N. C. Duke, E. Kristensen, S. Y. Lee, C.
Marchand, J. J. Middelburg, V. H. Rivera-Monroy, T. J. Smith, and R. R. Twilley. 2008. Mangrove production
and carbon sinks: A revision of global budget estimates. Global Biogeochemical Cycles 22.
Boyer, K. E., J. C. Callaway, and J. B. Zedler. 2000. Evaluating the progress of restored cordgrass (Spartina
foliosa) marshes: Belowground biomass and tissue nitrogen. Estuaries 23:711-721.
Breithaupt, J. L., J. M. Smoak, T. J. Smith, C. J. Sanders, and A. Hoare. 2012. Organic carbon burial rates inmangrove sediments: Strengthening the global budget. Global Biogeochemical Cycles 26.
Briggs, S. V. 1977.Estimaes of biomass in a temperate mangrove community. Journal of Ecology 2:369-373.
Brouns, J. 1985. A comparision of the annual production and biomass in 3 monospecific stands of the seagrass
Thalassia hemprichii (Ehrenb) Aschers. Aquatic Botany 23:149-175.
Brouns, J. 1987. Apects of production and biomass of 4 seagrass species (Cymodoceoideae) from Papua New
Guinea. Aquatic Botany 27:333-362.
Brouns, J., and F. M. L. Heijs. 1986. Production and biomass of the seagrass Enhalus acoroides (LF) Royle andits epiphytes. Aquatic Botany 25:21-45.

DRAFT 2013 Wetlands Supplement

4.37

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454

Brun, F. G., E. van Zetten, E. Cacabelos, and T. J. Bouma. 2009. Role of two contrasting ecosystem engineers(Zostera noltii and Cymodocea nodosa) on the food intake rate of Cerastoderma edule. Helgoland Marine
Research 63:19-25.
Cahoon, D. R., J. C. Lynch, and R. M. Knaus. 1996. Improved cryogenic coring device for sampling wetland
soils. Journal of Sedimentary Research 66:1025-1027.
Callaway, J. C., E. L. Borgnis, R. E. Turner, and C. S. Milan. 2012. Carbon Sequestration and Sediment
Accretion in San Francisco Bay Tidal Wetlands. Estuaries and Coasts 35:1163-1181.
Callaway, J. C., R. D. DeLaune, and W. H. Patrick. 1996. Chernobyl Cs-137 used to determine sedimentaccretion rates at selected northern European coastal wetlands. Limnology and Oceanography 41:444-450.
Callway, J., R.D., DeLaune, and W.H. Patrick. 1997. Sediment accretion rates from four coastal wetlands along
the Gulf of Mexico. Journal of Coastal Research 13: 181-191.
Calleja, M. L., C. Barron, J. A. Hale, T. K. Frazer, and C. M. Duarte. 2006. Light regulation of benthic sulfate
reduction rates mediated by seagrass (Thalassia testudinum) metabolism. Estuaries and Coasts 29:1255-1264.
Camacho, L. D., D. T. Gevaña, A. P. Carandang, S. C. Camacho, E. A. Combalicer, L. L. Rebugio, and Y.-C.
Youn. 2011. Tree biomass and carbon stock of a community‐managed mangrove forest in Bohol, Philippines.
Forest Science and Technology 7:161-167.
Camporese, M., M. Putti, P. Salandin, and P. Teatini. 2008. Spatial variability of CO2 efflux in a drained
cropped peatland south of Venice, Italy. Journal of Geophysical Research-Biogeosciences 113.
Cebrian, J., M. F. Pedersen, K. D. Kroeger, and I. Valiela. 2000. Fate of production of the seagrass Cymodocea
nodosa in different stages of meadow formation. Marine Ecology Progress Series 204:119-130.
Chave, J., C. Andalo, S. Brown, M. A. Cairns, J. Q. Chambers, D. Eamus, H. Folster, F. Fromard, N. Higuchi, T.
Kira, J. P. Lescure, B. W. Nelson, H. Ogawa, H. Puig, B. Riera, and T. Yamakura. 2005. Tree allometry andimproved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87-99.
Chen, G. C., Y. Ye, and C. Y. Lu. 2008. Seasonal variability of leaf litter removal by crabs in a Kandelia candelmangrove forest in JiulongJiang Estuary, China. Estuarine Coastal and Shelf Science 79:701-706.
Chmura, G. L., S. C. Anisfeld, D. R. Cahoon, and J. C. Lynch. 2003. Global carbon sequestration in tidal, saline
wetland soils. Global Biogeochemical Cycles 17.
Chmura, G. L., and G. A. Hung. 2004. Controls on salt marsh accretion: A test in salt marshes of Eastern Canada.
Estuaries 27:70-81.
Christensen, B. 1978. Biomass and primary production of Rhizophora apiculata-BL in a mangrove in southern
Thailand. Aquatic Botany 4:43-52.
Coles, R. G., W. J. L. Long, R. A. Watson, and K. J. Derbyshire. 1993. Distribution of seagrasses, and their fishand penaeid prawn communities, in Cairns Harbor, a tropical estuary, Northern Queensland, Australia.
Australian Journal of Marine and Freshwater Research 44:193-210.
Collier, C. J., P. S. Lavery, P. J. Ralph, and R. J. Masini. 2009. Shade-induced response and recovery of theseagrass Posidonia sinuosa. Journal of Experimental Marine Biology and Ecology 370:89-103.
Connor, R., and G. L. Chmura. 2000. Dynamics of above- and belowground organic matter in a high latitude
macrotidal saltmarsh. Marine Ecology Progress Series 204:101-110.
Connor, R. F., G. L. Chmura, and C. B. Beecher. 2001. Carbon accumulation in Bay of Fundy salt marshes:
Implications for restoration of reclaimed marshes. Global Biogeochemical Cycles 15:943-954.
Coronado-Molina, C., J. Day, E. Reyes, and B. Perez. 2004. Standing crop and aboveground biomass
partitioning of a dwarf mangrove forest in Taylor River Slough, Florida. Wetlands Ecology and Management
12:157-164.
Craft, C. 2007. Freshwater input structures soil properties, vertical accretion, and nutrient accumulation ofGeorgia and U.S. tidal marshes. Limnology and Oceanography 52:1220-1230.
Craft, C., S. Broome, and C. Campbell. 2002. Fifteen years of vegetation and soil development after brackishwater marsh creation. Restoration Ecology 10:248-258.
Craft, C., J. Reader, J. N. Sacco, and S. W. Broome. 1999. Twenty-five years of ecosystem development ofconstructed Spartina alterniflora (Loisel) marshes. Ecological Applications 9:1405-1419.
Craft, C., P. Megonigal, S. Broome, J. Stevenson, R. Freese, J. Cornell, L. Zheng, and J. Sacco. 2003. The paceof ecosystem development of constructed Spartina alterniflora marshes. Ecological Applications 13:1417-1432.
Craft, C. B., S. W. Broome, and E. D. Seneca. 1988. Nitrogen, phosphrous and organic carbon pools in naturaland transplanted marsh soils. Estuaries 11:272-280.

4.38

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507

Cruz, P. S. 1997. Aquaculture feed and fertilizer resource atlas of the Philippines. FAO.
da Cunha Lana, P., C. Guiss, and S. T. Disaro. 1991. Seasonal variation of biomass and production dynamics forabove- and belowground components of a Spartina alterniflora marsh in the euhaline sector of Paranagua Bay(SE Brazil). Estuarine Coastal and Shelf Science 32:231-241.
Daby, D. 2003. Effects of seagrass bed removal for tourism purposes in a Mauritian bay. Environmental
Pollution 125:313-324.
de Boer, W. F. 2000. Biomass dynamics of seagrasses and the role of mangrove and seagrass vegetation as
different nutrient sources for an intertidal ecosystem. Aquatic Botany 66:225-239.
Deverel, S.J. Leighton, D.A. 2010. Historic, recent, and future subsidence, Sacramento-San Joaquin Delta,
California. USA. San Francisco Estuary and Watershed Science 8.
Devereux, R., D. F. Yates, J. Aukamp, R. L. Quarles, S. J. Jordan, R. S. Stanley, and P. M. Eldridge. 2011.
Interactions of Thalassia testudinum and sediment biogeochemsistry in Santa Rosa Sound, NW Florida. Marine
Biology Research 7:317-331.
Dittmar, T., and R. J. Lara. 2001. Molecular evidence for lignin degradation in sulfate-reducing mangrove
sediments (Amazonia, Brazil). Geochimica Et Cosmochimica Acta 65:1417-1428.
Dixon, L. K., and J. R. Leverone. 1995. Light requirements of Thalassia testudinum in Tampa Bay, Florida: final
report.
Donato, D. C., J. B. Kauffman, R. A. Mackenzie, A. Ainsworth, and A. Z. Pfleeger. 2012. Whole-island carbon
stocks in the tropical Pacific: Implications for mangrove conservation and upland restoration. Journal ofEnvironmental Management 97:89-96.
Donato, D. C., J. B. Kauffman, D. Murdiyarso, S. Kurnianto, M. Stidham, and M. Kanninen. 2011. Mangroves
among the most carbon-rich forests in the tropics. Nature Geoscience 4:293-297.
Dos Santos, V. M., F. E. Matheson, C. A. Pilditch, and A. Elger. 2012. Is black swan grazing a threat to seagrass?
Indications from an observational study in New Zealand. Aquatic Botany 100:41-50.
Dunton, K. H. 1996. Photosynthetic production and biomass of the subtropical seagrass Halodule wrightii along
an estuarine gradient. Estuaries 19:436-447.
Dürr H.H., G.G Laruelle., C.M. van Kempen, C.P. Slomp, M. Meybeck, H. Middelkoop. 2011. World-wide
typology of near-shore coastal systems: defining the estuarine filter of river inputs to the oceans. Estuaries andCoasts, 34(3), 441-458, doi:10.1007/s12237-011-9381-y.
Ellison, A. M., M. D. Bertness, and T. Miller. 1986. Seasonal patterns in the belowground biomass of Spartina
alterniflora (Gramineae) across a tidal gradient. American Journal of Botany 73:1548-1554.
Ellison, A. M., and E. J. Farnsworth. 1996. Anthropogenic disturbance of Caribbean mangrove ecosystems: Past
impacts, present trends, and future predictions. Biotropica 28:549-565.
Elsey-Quirk, T., D. M. Seliskar, C. K. Sommerfield, and J. L. Gallagher. 2011. Salt Marsh Carbon Pool
Distribution in a Mid-Atlantic Lagoon, USA: Sea Level Rise Implications. Wetlands 31:87-99.
Fatoyinbo, T. E., and M. Simard. 2013. Height and biomass of mangroves in Africa from ICESat/GLAS and
SRTM. International Journal of Remote Sensing 34:668-681.
Fatoyinbo, T. E., M. Simard, R. A. Washington-Allen, and H. H. Shugart. 2008. Landscape-scale extent, height,
biomass, and carbon estimation of Mozambique's mangrove forests with Landsat ETM+ and Shuttle Radar
Topography Mission elevation data. Journal of Geophysical Research-Biogeosciences 113.
Filip, Z., J. J. Alberts, M. V. Cheshire, B. A. Goodman, and J. R. Bacon. 1988. Comparision of salt marsh humic
acid with humic-like substances from the indigenous plant species Spartina alterniflora (Loisel). Science of the
Total Environment 71:157-172.
Food and Agriculture Organization of the United Nations (FAO). 2003. Status and trends in mangrove area
extent worldwide, FAO, Paris, 2003.
Food and Agriculture Organization of the United Nations. 2007. The World's Mangroves 1980-2005. FAO
Forestry Paper 153.
Fourqurean, J. W., C. M. Duarte, H. Kennedy, N. Marba, M. Holmer, M. A. Mateo, E. T. Apostolaki, G. A.
Kendrick, D. Krause-Jensen, K. J. McGlathery, and O. Serrano. 2012. Seagrass ecosystems as a globallysignificant carbon stock. Nature Geoscience 5:505-509.
Fourqurean, J. W., T. O. Moore, B. Fry, and J. T. Hollibaugh. 1997. Spatial and temporal variation in C:N:P
ratios, delta N-15 and delta C-13 of eelgrass Zostera marina as indicators of ecosystem processes, Tomales Bay,
California, USA. Marine Ecology Progress Series 157:147-157.

DRAFT 2013 Wetlands Supplement

4.39

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560

Fromard, F., H. Puig, E. Mougin, G. Marty, J. L. Betoulle, and L. Cadamuro. 1998. Structure, above-groundbiomass and dynamics of mangrove ecosystems: new data from French Guiana. Oecologia 115:39-53.
Fujimoto, K., A. Imaya, R. Tabuchi, S. Kuramoto, H. Utsugi, and T. Murofushi. 1999. Belowground carbon
storage of Micronesian mangrove forests. Ecological Research 14:409-413.
Gedan, K. B., B. R. Silliman, and M. D. Bertness. 2009. Centuries of Human-Driven Change in Salt Marsh
Ecosystems. Annual Review of Marine Science 1:117-141.
Golley, F.B., H.T. Odum, and R.F. Wilson. 1962. The structure and metabolism of a Puerto Rican red mangrove
forest ecosystem. University of Georgia Press, Athens, GA, USA.
Golley, F.B., K. McGuiniss, R.G. Clements, G.I. Child, M.J. Duever. 1975. Mineral cycling in a tropical moist
ecosystem. University of Georgia Press, Athens, GA, USA.
Gong, W. K., and J. E. Ong. 1990. Plant biomass and nutrient flux in a managed mangrove in Malaysia.
Estuarine Coastal and Shelf Science 31:519-530.
Gross, M. F., M. A. Hardisky, P. L. Wolf, and V. Klemas. 1991. Relationship between aboveground andbelowground biomass of Spartina alterniflora (smooth cordgrass). Estuaries 14:180-191.
Hackney, J. W. 2003. Morphometric variability and allometric relationships in the seagrass Thalassia testudinumin Florida Bay. University of North Carolina.
Halun, Z., J. Terrados, J. Borum, L. Kamp-Nielsen, C. M. Duarte, and M. D. Fortes. 2002. Experimental
evaluation of the effects of siltation-derived changes in sediment conditions on the Philippine seagrass
Cymodocea rotundata. Journal of Experimental Marine Biology and Ecology 279:73-87.
Harris, R. J., E. C. Milbrandt, E. M. Everham, and B. D. Bovard. 2010. The Effects of Reduced Tidal Flushingon Mangrove Structure and Function Across a Disturbance Gradient. Estuaries and Coasts 33:1176-1185.
Hatala, J. A., M. Detto, O. Sonnentag, S. J. Deverel, J. Verfaillie, and D. D. Baldocchi. 2012. Greenhouse gas(CO2, CH4, H2O) fluxes from drained and flooded agricultural peatlands in the Sacramento-San Joaquin Delta.
Agriculture Ecosystems & Environment 150:1-18.
Hatton, R. S., R. D. Delaune, and W. H. Patrick. 1983. Sedimentation, accretion, and subsidence in marshes ofBarataria Basin, Louisiana. Limnology and Oceanography 28:494-502.
Hebert, A. B., J. W. Morse, and P. M. Eldridge. 2007. Small-scale heterogeneity in the geochemistry of seagrass
vegetated and non-vegetated estuarine sediments: causes and consequences. Aquatic Geochemistry 13:19-39.
Herbert, D. A. 1986. The growth dynamics of Halophila Hawaiiana. Aquatic Botany 23:351-360.
Herbert, D. A., and J. W. Fourqurean. 2008. Ecosystem structure and function still altered two decades after
short-term fertilization of a seagrass meadow. Ecosystems 11:688-700.
Herbert, D. A., and J. W. Fourqurean. 2009. Phosphorus Availability and Salinity Control Productivity and
Demography of the Seagrass Thalassia testudinum in Florida Bay. Estuaries and Coasts 32:188-201.
Holmer, M., F. O. Andersen, S. L. Nielsen, and H. T. S. Boschker. 2001. The importance of mineralization basedon sulfate reduction for nutrient regeneration in tropical seagrass sediments. Aquatic Botany 71:1-17.
Holmer, M., and G. A. Kendrick. 2012. High Sulfide Intrusion in Five Temperate Seagrasses Growing Under
Contrasting Sediment Conditions. Estuaries and Coasts 36:116-126.
Hoque, A., S. Sharma, R. Suwa, S. Mori, and A. Hagihara. 2012. Seasonal variation in the size-dependent
respiration of mangroves Kandelia obovata (vol 404, pg 31, 2010). Marine Ecology Progress Series 467:303303.
Howe, A. J., J. F. Rodriguez, and P. M. Saco. 2009. Surface evolution and carbon sequestration in disturbed andundisturbed wetland soils of the Hunter estuary, southeast Australia. Estuarine Coastal and Shelf Science
84:75-83.
Hu, Z., J. W. Lee, K. Chandran, S. Kim, and S. K. Khanal. 2012. Nitrous Oxide (N2O) Emission from
Aquaculture: A Review. Environmental Science & Technology 46:6470-6480.
Hussein, A. H., M. C. Rabenhorst, and M. L. Tucker. 2004. Modeling of carbon sequestration in coastal marsh
soils. Soil Science Society of America Journal 68:1786-1795.
Hussey, A., and S. P. Long. 1982. Seasonal changes in weight of above- and below-ground vegetation and deadplant material in a salt marsh at Colne Point, Essex. Journal of Ecology 70:757-771.
Ismail, N. 1993. Preliminary study of the seagrass flora of Sabah, Malaysia. Pertanika Journal of Tropical
Agricultural Science 16:111-118.
Jensen, S., and S. Bell. 2001. Seagrass growth and patch dynamics: cross-scale morphological plasticity. Plant
Ecology 155:201-217.

4.40

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613

Jones, D. A., M. Nithyanandan, and I. Williams. 2012. Sabah Al-Ahmad Sea City Kuwait: development of asustainable man-made coastal ecosystem in a saline desert. Aquatic Ecosystem Health & Management 15:8492.
Juliana, W. A., and M. S. Nizam. 2004. Forest structure and above-ground biomass of two mangrove forest
communities in Matang, Ipoh, Perak, Malaysia.
Kairo, J. G., J. K. S. Lang'at, F. Dahdouh-Guebas, J. Bosire, and M. Karachi. 2008. Structural development andproductivity of replanted mangrove plantations in Kenya. Forest Ecology and Management 255:2670-2677.
Kauffman, J. B., C. Heider, T. G. Cole, K. A. Dwire, and D. C. Donato. 2011. Ecosystem Carbon Stocks ofMicronesian Mangrove Forests. Wetlands 31:343-352.
Kearney, M. S., and J. C. Stevenson. 1991. Island land loss and marsh vertical accretion rate evidence forhistorical sea-level changes in Chesapeake Bay. Journal of Coastal Research 7:403-415.
Khan, M. N. I., R. Suwa, and A. Hagihara. 2009. Biomass and aboveground net primary production in a
subtropical mangrove stand of Kandelia obovata (S., L.) Yong at Manko Wetland, Okinawa, Japan. Wetlands
Ecology and Management 17:585-599.
Kim, S. H., Y. K. Kim, S. R. Park, W. T. Li, and K. S. Lee. 2012. Growth dynamics of the seagrass Halophila
nipponica, recently discovered in temperate coastal waters of the Korean peninsula. Marine Biology 159:255267.
Kirkman, H., and D. D. Reid. 1979. Study of the role of the seagrass Posidonia australis in the carbon budget ofan estuary. Aquatic Botany 7:173-183.
Kirue, B., J. Kairo, and M. Karachi. 2007. Allometric Equations for Estimating Above Ground Biomass of
Rhizophora mucronata Lamk.(Rhizophoraceae) Mangroves at Gaxi Bay, Kenya. Western Indian Ocean Journalof Marine Science 5:27-34.
Kistritz, R. U., K. J. Hall, and I. Yesaki. 1983. Productivity, detritus flux, and nutrient cycling in a Carex
lyngbyei tidal marsh. Estuaries 6:227-236.
Koch, B. P., P. W. M. Souza, H. Behling, M. C. L. Cohen, G. Kattner, J. Rullkotter, B. Scholz-Bottcher, and R. J.
Lara. 2011. Triterpenols in mangrove sediments as a proxy for organic matter derived from the red mangrove(Rhizophora mangle). Organic Geochemistry 42:62-73.
Komiyama, A., S. Havanond, W. Srisawatt, Y. Mochida, K. Fujimoto, T. Ohnishi, S. Ishihara, and T. Miyagi.
2000. Top/root biomass ratio of a secondary mangrove (Ceriops tagal (Perr.) CB Rob.) forest. Forest Ecology
and Management 139:127-134.
Komiyama, A., H. Moriya, S. Prawiroatmodjo, T. Toma, and K. Ogino. 1988. Primary productivity of mangrove
forest, Ehime University, Ehime, Japan.
Komiyama, A., K. Ogino, S. Aksornkoae, and S. Sabhasri. 1987. Root biomass of a mangrove forest in southern
Thailand. 1. Estimation by the trench method and the zonal structure of root biomass. Journal of Tropical
Ecology 3:97-108.
Komiyama, A., J. E. Ong, and S. Poungparn. 2008. Allometry, biomass, and productivity of mangrove forests: A
review. Aquatic Botany 89:128-137.
Kowalski, J. L., H. R. DeYoe, and T. C. Allison. 2009. Seasonal Production and Biomass of the Seagrass,
Halodule wrightii Aschers. (Shoal Grass), in a Subtropical Texas Lagoon. Estuaries and Coasts 32:467-482.
Krauss, K. W., T. W. Doyle, R. R. Twilley, T. J. Smith, K. R. T. Whelan, and J. K. Sullivan. 2005. Woody
debris in the mangrove forests of South Florida. Biotropica 37:9-15.
Kristensen, E., S. Bouillon, T. Dittmar, and C. Marchand. 2008a. Organic carbon dynamics in mangrove
ecosystems: A review. Aquatic
Kungvankij, P., B. Pudadera Jr, L. Tiro Jr, I. Potestas, and T. Chua. 1986. An improved traditional shrimp
culture technique for increasing pond yield.
Kusmana, C., S. Sabiham, K. Abe, and H. Watanabe. 1992. An estimation of above ground tree biomass of amangrove forest in East Sumatra, Indonesia. Tropics 1:243-257.
Larkum, A. W. D., L. C. Collett, and R. J. Williams. 1984. The standing stock, growth and shoot production of
Zostera capricornia (Aschers) in Botany Bay, New South Wales. Aquatic Botany 19:307-327.
Larkum, A. W. D., and R. J. West. 1990. Long-term changes of seagrass meadows in Botany Bay, Australia.
Aquatic Botany 37:55-70.
Larned, S. T. 2003. Effects of the invasive, nonindigenous seagrass Zostera japonica on nutrient fluxes between
the water column and benthos in a NE Pacific estuary. Marine Ecology Progress Series 254:69-80.

DRAFT 2013 Wetlands Supplement

4.41

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666

Lebreton, B., P. Richard, G. Radenac, M. Bordes, M. Breret, C. Arnaud, F. Mornet, and G. F. Blanchard. 2009.
Are epiphytes a significant component of intertidal Zostera noltii beds? Aquatic Botany 91:82-90.
Lee, K. S., S. R. Park, and J. B. Kim. 2005a. Production dynamics of the eelgrass, Zostera marina in two bay
systems on the south coast of the Korean peninsula. Marine Biology 147:1091-1108.
Lee, S. Y. 1997. Annual cycle of biomass of a threatened population of the intertidal seagrass Zostera japonica inHong Kong. Marine Biology 129:183-193.
Lee, S. Y., J. H. Oh, C. I. Choi, Y. Suh, and H. Mukai. 2005b. Leaf growth and population dynamics of intertidal
Zostera japonica on the western coast of Korea. Aquatic Botany 83:263-280.
Liao, B., D. Zheng, and S. Zheng. 1990. Studies on the biomass of Sonneratia caseolaris stand. Forest Research
3:47-54.
Lillebo, A. I., M. R. Flindt, M. A. Pardal, and J. C. Marques. 2006. The effect of Zostera noltii, Spartina
maritima and Scirpus maritimus on sediment pore-water profiles in a temperate intertidal estuary.
Hydrobiologia 555:175-183.
Lin, P. 1989. Biomass and element cycle of Kandelia forest in China. Xiamen Univ. Press, Xiamen.
Lin, P., C. Lu, G. Wang, and H. Chen. 1990. Biomass and productivity of Bruguiera sexangula mangrove forest
in Hainan Island, China. Journal of Xiamen University (Natural Science) 29:209-213.
Lindeboom, H. J., and A. J. J. Sandee. 1989. Production and consumption of tropical seagrass fields in Eastern
Indoneisa measured with bell jars and microelectrodes. Netherlands Journal of Sea Research 23:181-190.
Lipkin, Y. 1979. Quantitative aspects of seagrass communities, particularly of those dominated by Halophila
stipulacea, in Sinai (Northern Red Sea). Aquatic Botany 7:119-128.
Livesley, S., S. Andrusiak, and D. Idczak. 2010. Soil greenhouse gas exchange and carbon stocks in natural andmanaged ecosystems of the Mornington Peninsula. Melbourne, Australia.
Longstaff, B. J., and W. C. Dennison. 1999. Seagrass survival during pulsed turbidity events: the effects of light
deprivation on the seagrasses Halodule pinifolia and Halophila ovalis. Aquatic Botany 65:105-121.
Loomis, M. J., and C. B. Craft. 2010. Carbon Sequestration and Nutrient (Nitrogen, Phosphorus) Accumulationin River-Dominated Tidal Marshes, Georgia, USA. Soil Science Society of America Journal 74:1028-1036.
Lovelock, C. E., R. W. Ruess, and I. C. Feller. 2011. CO2 Efflux from Cleared Mangrove Peat. Plos One 6.
Mackey, A. P. 1993. Biomass of the mangrove Avicennia marina (Forsk) near Brisvance, Southeastern
Queensland. Australian Journal of Marine and Freshwater Research 44:721-725.
Madkour, F. F., and M. M. Gaballah. 2012. Phytoplankton assemblage of a solar saltern in Port Fouad, Egypt.
Oceanologia 54:687-700.
Mahall, B. E., and R. B. Park. 1976. The ecotone between Spartina foliosa Trin. and Salicornia virginica L. insalt marshes of Northern San Francisco Bay: II. Soil water and salinity. Journal of Ecology 64:793-809.
Maher, D.T., I.R. Santos, L. Golsby-Smith, J. Gleeson, and B.D. Eyre. 2013. Groundwater-derived dissolved
inorganic and organic carbon exports from a mangrove tidal creek: The missing mangrove carbon sink?
Limnology and Oceanography 58: 475-488.
Marba, N., and C. M. Duarte. 2001. Growth and sediment space occupation by seagrass Cymodocea nodosaroots. Marine Ecology Progress Series 224:291-298.
Marchand, C., J. R. Disnar, E. Lallier-Verg, and N. Lottier. 2005. Early diagenesis of carbohydrates and lignin in
mangrove sediments subject to variable redox conditions (French Guiana). Geochimica Et Cosmochimica Acta
69:131-142.
Marchand, C., E. Lallier-Verges, and F. Baltzer. 2003. The composition of sedimentary organic matter in
relation to the dynamic features of a mangrove-fringed coast in French Guiana. Estuarine Coastal and Shelf
Science 56:119-130.
Markewich, H. W., D. A. Wysocki, M. J. Pavich, E. M. Rutledge, H. T. Millard, F. J. Rich, P. B. Maat, M. Rubin,
and J. P. McGeehin. 1998. Paleopedology plus TL, Be-10, and C-14 dating as tools in stratigraphic andpaleoclimatic investigations, Mississippi River Valley, USA. Quaternary International 51-2:143-167.
Mateo, M. A., and J. Romero. 1997. Detritus dynamics in the seagrass Posidonia oceanica: Elements for an
ecosystem carbon and nutrient budget. Marine Ecology Progress Series 151:43-53.
Matsui, N., K. Morimune, W. Meepol, and J. Chukwamdee. 2012. Ten Year Evaluation of Carbon Stock in
Mangrove Plantation Reforested from an Abandoned Shrimp Pond. Forests 3:431-444.
McCaffrey, R. J., and J. Thomson. 1980. A Record of the Accumulation of Sediment and Trace Metals in A
Connecticut Salt Marsh. Pages 165-236 in S. Barry, editor. Advances in Geophysics. Elsevier.

4.42

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720

McGlathery, KJ, Reynolds, LK, Cole, LW, Orth, RJ, Marion, SR, Schwarzschild, A. 2012. Recovery trajectories
during state change from bare sediment to eelgrass dominance. Marine Ecology Progress Series 448: 209-221.
McKenzie, L. 1994. Seasonal changes in biomass and shoot characteristics of a Zostera capricorni Aschers.
Dominant meadow in Cairns Harbour, northern Queensland. Marine and Freshwater Research 45:1337-1352.
McMahan, C. A. 1968. Biomass and Salinity Tolerance of Shoalgrass and Manateegrass in Lower Laguna Madre,Texas. The Journal of Wildlife Management 32:501-506.
McRoy, C. P. 1974. Seagrass productivity: Carbon uptake experiments in eelgrass, Zostera marina. Aquaculture
4:131-137.
Medeiros, T., and E. Sampaio. 2008. Allometry of aboveground biomasses in mangrove species in Itamaracá,
Pernambuco, Brazil. Wetlands Ecology and Management 16:323-330.
Meling-Lopez, A. E., and S. E. Ibarra-Obando. 1999. Annual life cycles of two Zostera marina L-populations in
the Gulf of California: contrasts in seasonality and reproductive effort. Aquatic Botany 65:59-69.
Mellors, J., H. Marsh, T. J. Carruthers, and M. Waycott. 2002. Testing the sediment-trapping paradigm ofseagrass: Do seagrasses influence nutrient status and sediment structure in tropical intertidal environments?
Bulletin of Marine Science 71:1215-1226.
Moriarty, D. J. W., D. G. Roberts, and P. C. Pollard. 1990. Primary and bacterial productivity of tropicalseagrass communities in the Gulf of Carpentaria, Australia. Marine Ecology Progress Series 61:145-157.
Morris, J. T., J. Edwards, S. Crooks, and E. Reyes. 2012. Assessment of carbon sequestration potential in coastal
wetlands. Pages 517-531 in R. Lal, K. Lorenz, R. F. Huttl, B. U. Schneider, and J. von Braun, editors.
Recarbonization of the Biosphere: Ecosystems and the Global Carbon Cycle. Springer, Dordrecht.
Mukai, H., K. Aioi, I. Koike, H. Iizumi, M. Ohtsu, and A. Hattori. 1979. Growth and organic production of
eelgrass ( Zostera marina L.) in temperate waters of the pacific coast of Japan. I. Growth analysis in spring–summer. Aquatic Botany 7:47-56.
Neves, J. P., L. F. Ferreira, M. P. Simoes, and L. C. Gazarini. 2007. Primary production and nutrient content in
two salt marsh species, Atriplex portulacoides L. and Limoniastrum monopetalum L., in Southern Portugal.
Estuaries and Coasts 30:459-468.
Nienhuis, P., J. Coosen, and W. Kiswara. 1989. Community structure and biomass distribution of seagrasses andmacrofauna in the Flores Sea, Indonesia. Netherlands Journal of Sea Research 23:197-214.
Norhayati, A., and A. Latiff. 2001. Biomass and species composition of a mangrove forest in Pulau Langkawi,
Malaysia. Malaysian Applied Biology 30:75-80.
Odum, H. T. 1963. Productivity measurements in Texas turtle grass and the effects of dredging an intracoastal
channel. Publications of the Institute of Marine Science 9:48-58.
Oenema, O., and R. D. DeLaune. 1988. Accretion rates in salt marshes in the Eastern Scheldt, south-west
Netherlands. Estuarine, Coastal and Shelf Science 26:379-394.
Ogden, J. C., and N. B. Ogden. 1982. A preliminary study of two representative seagrass communities in Palau,
Western Caroline Islands (Micronesia). Aquatic Botany 12:229-244.
Olesen, B., and K. Sand-Jensen. 1994. Biomass-density patterns in the temperate seagrass Zostera marina.
Marine Ecology-Progress Series 109:283-283.
Ong, E. 1982. Mangroves and aquaculture in Malaysia. Ambio 11:252-257.
Oren, A. 2009. Saltern evaporation ponds as model systems for the study of primary production processes under
hypersaline conditions. Aquatic Microbial Ecology 56:193-204.
Orson, R., R. Warren, and W. Niering. 1998. Interpreting sea level rise and rates of vertical marsh accretion in asouthern New England tidal salt marsh. Estuarine, Coastal and Shelf Science 47:419-429.
Orth, R. J., K. A. Moore, S. R. Marion, D. J. Wilcox, and D. B. Parrish. 2011. Seed addition facilitates eelgrass
recovery in a coastal bay system. Marine Ecology Progress Series 448:177-195.
Ortiz-Milán, S. 2009. Project of Recovery the Biological Conditions of the Production System in Saltworks ofIndustria Salinera de Yucatan SA de CV (ISYSA) Damaged by the Hurricane Isidore in September of 2002.
Global NEST Journal 11:91-95.
Osborne, T. Z., G. L. Bruland, S. Newman, K. R. Reddy, and S. Grunwald. 2011. Spatial distributions and ecopartitioning of soil biogeochemical properties in the Everglades National Park. Environmental monitoring andassessment 183:395-408.
Osland, M. J., A. C. Spivak, J. A. Nestlerode, J. M. Lessmann, A. E. Almario, P. T. Heitmuller, M. J. Russell, K.
W. Krauss, F. Alvarez, and D. D. Dantin. 2012. Ecosystem development after mangrove wetland creation:
plant–soil change across a 20-year chronosequence. Ecosystems 15:848-866.

DRAFT 2013 Wetlands Supplement

4.43

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773

Paling, E. I., and A. J. McComb. 2000. Autumn biomass, below-ground productivity, rhizome growth at bededge and nitrogen content in seagrasses from Western Australia. Aquatic Botany 67:207-219.
Park, S. R., Y. K. Kim, J.-H. Kim, C.-K. Kang, and K.-S. Lee. 2011. Rapid recovery of the intertidal seagrass
Zostera japonica following intense Manila clam (Ruditapes philippinarum) harvesting activity in Korea.
Journal of Experimental Marine Biology and Ecology 407:275-283.
Patrick Jr, W. H., and R. DeLaune. 1990. Subsidence, accretion, and sea level rise in south San Francisco Bay
marshes. Limnology and Oceanography 35:1389-1395.
Paynter, C. K., J. Cortés, and M. Engels. 2001. Biomass, productivity and density of the seagrass Thalassia
testudinum at three sites in Cahuita National Park, Costa Rica. Rev. Biol. Trop 49:265-272.
Pendleton, L., D. C. Donato, B. C. Murray, S. Crooks, W. A. Jenkins, S. Sifleet, C. Craft, J. W. Fourqurean, J. B.
Kauffman, N. Marba, P. Megonigal, E. Pidgeon, D. Herr, D. Gordon, and A. Baldera. 2012. Estimating Global"Blue Carbon" Emissions from Conversion and Degradation of Vegetated Coastal Ecosystems. PLOS One 7.
Perry, C. L., and I. A. Mendelssohn. 2009. Ecosystem effects of expanding populations of Avicennia germinans
in a Louisiana salt marsh. Wetlands 29:396-406.
Pidgeon, E. 2009. Carbon sequestration by coastal marine habitats: Missing sinks, In the Management of
Natural Coastal Carbon Sinks
edited by D. d’A Laffoley and G. Grimsditch, pp. 47–51, IUCN, Gland, Switzerland.
Poffenbarger, H. J., B. A. Needelman, and J. P. Megonigal. 2011. Salinity influence on methane emissions fromtidal marshes. Wetlands 31:831-842.
Poovachiranon, S., and H. Chansang. 1994. Community structure and biomass of seagrass beds in the Andaman
Sea. I. Mangrove-associated seagrass beds. Phuket Marine Biological Center Research Bulletin 59:53-64.
Povidisa, K., M. Delefosse, and M. Holmer. 2009. The formation of iron plaques on roots and rhizomes of theseagrass Cymodocea serrulata (R. Brown) Ascherson with implications for sulphide intrusion. Aquatic Botany
90:303-308.
Powell, G. V., J. W. Kenworthy, and J. W. Fourqurean. 1989. Experimental evidence for nutrient limitation ofseagrass growth in a tropical estuary with restricted circulation. Bulletin of Marine Science 44:324-340.
Pozo, J., and R. Colino. 1992. Decomposition processes of Spartina maritima in a salt marsh of the BasqueCountry. Hydrobiologia 231:165-175.
Prasad, M. B., and A. L. Ramanathan. 2009. Organic matter characterization in a tropical estuarine-mangrove
ecosystem of India: Preliminary assessment by using stable isotopes and lignin phenols. Estuarine, Coastal and
Shelf Science 84:617-624.
Preen, A. 1995. Impacts of dugong foraging on seagrass habitats: observational and experimental evidence forcultivation grazing. Marine ecology progress series. Oldendorf 124:201-213.
Purvaja, R., and R. Ramesh. 2001. Natural and anthropogenic methane emission from coastal wetlands of South
India. Environmental Management 27:547-557.
Putz, F. E., and H. Chan. 1986. Tree growth, dynamics, and productivity in a mature mangrove forest in
Malaysia. Forest Ecology and Management 17:211-230.
Ramos e Silva, C. A., S. R. Oliveira, R. D. Rêgo, and A. A. Mozeto. 2007. Dynamics of phosphorus andnitrogen through litter fall and decomposition in a tropical mangrove forest. Marine environmental research
64:524-534.
Ranjan, R. K., J. Routh, A. Ramanathan, and J. V. Klump. 2011. Elemental and stable isotope records of organic
matter input and its fate in the Pichavaram mangrove–estuarine sediments (Tamil Nadu, India). Marine
Chemistry 126:163-172.
Rasheed, M. A. 1999. Recovery of experimentally created gaps within a tropical Zostera capricorni (Aschers.)
seagrass meadow, Queensland Australia. Journal of Experimental Marine Biology and Ecology 235:183-200.
Ray, R., D. Ganguly, C. Chowdhury, M. Dey, S. Das, M. Dutta, S. Mandal, N. Majumder, T. De, and S.
Mukhopadhyay. 2011. Carbon sequestration and annual increase of carbon stock in a mangrove forest.
Atmospheric Environment 45:5016-5024.
Ren, H., H. Chen, Z. a. Li, and W. Han. 2010. Biomass accumulation and carbon storage of four different aged
Sonneratia apetala plantations in Southern China. Plant and soil 327:279-291.
Richards, T. M., J. M. Krebs, and C. C. McIvor. 2011. Microhabitat associations of a semi-terrestrial fish,
Kryptolebias marmoratus (Poey 1880) in a mosquito-ditched mangrove forest, west-central Florida. Journal of
Experimental Marine Biology and Ecology 401:48-56.

4.44

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827

Rismondo, A., D. Curiel, M. Marzocchi, and M. Scattolin. 1997. Seasonal pattern of< i> Cymodocea nodosa</i>biomass and production in the lagoon of Venice. Aquatic botany 58:55-64.
Robertson, A., and M. Phillips. 1995. Mangroves as filters of shrimp pond effluent: predictions andbiogeochemical research needs. Hydrobiologia 295:311-321.
Robertson, A. I., and P. A. Daniel. 1989. Decomposition and the annual flux of detritus from fallen timber intropical mangrove forests. Limnology and Oceanography 34:640-646.
Rojstaczer, S., and S. J. Deverel. 1993. Time dependence in atmospheric carbon inputs from drainage of organicsoils. Geophysical Research Letters 20:1383-1386.
Roman, C., J. Peck, J. Allen, J. King, and P. Appleby. 1997. Accretion of a New England (USA) salt marsh in
response to inlet migration, storms, and sea-level rise. Estuarine, Coastal and Shelf Science 45:717-727.
Ross, M. S., P. L. Ruiz, G. J. Telesnicki, and J. F. Meeder. 2001. Estimating above-ground biomass andproduction in mangrove communities of Biscayne National Park, Florida (USA). Wetlands Ecology andManagement 9:27-37.
Saenger, P. 2002. Mangrove ecology, silviculture and conservation. Springer, Kluwer, Dordecht.
Saintilan, N. 1997. Above-and below-ground biomasses of two species of mangrove on the Hawkesbury Riverestuary, New South Wales. Marine and freshwater research 48:147-152.
Sand-Jensen, K., and J. Borum. 1983. Regulation of growth of eelgrass (Zostera marina L.) in Danish coastal
waters. Marine Technology Society Journal 17:15-21.
Scarton, F., J. W. Day, and A. Rismondo. 2002. Primary production and decomposition of Sarcocornia fruticosa(L.) Scott and Phragmites australis Trin. ex Steudel in the Po Delta, Italy. Estuaries 25:325-336.
Schwarz, A.-M., M. Morrison, I. Hawes, and J. Halliday. 2006. Physical and biological characteristics of a raremarine habitat: sub-tidal seagrass beds of offshore islands. Department of Conservation.
Setia, R., P. Marschner, J. Baldock, D. Chittleborough, and V. Verma. 2011. Relationships between carbon
dioxide emission and soil properties in salt-affected landscapes. Soil Biology and Biochemistry 43:667-674.
Sherman, R. E., T. J. Fahey, and P. Martinez. 2003. Spatial patterns of biomass and aboveground net primary
productivity in a mangrove ecosystem in the Dominican Republic. Ecosystems 6:384-398.
Simard, M., K. Zhang, V. H. Rivera-Monroy, M. S. Ross, P. L. Ruiz, E. Castañeda-Moya, R. R. Twilley, and E.
Rodriguez. 2006. Mapping height and biomass of mangrove forests in Everglades National Park with SRTM
elevation data. Photogrammetric Engineering and Remote Sensing 72:299-311.
Slim, F., P. Gwada, M. Kodjo, and M. Hemminga. 1996. Biomass and litterfall of Ceriops tagal and Rhizophoramucronata in the mangrove forest of Gazi Bay, Kenya. Marine and freshwater research 47:999-1007.
Smith III, T. J., and K. R. Whelan. 2006. Development of allometric relations for three mangrove species inSouth Florida for use in the Greater Everglades Ecosystem restoration. Wetlands Ecology and Management
14:409-419.
Smith, K. K., R. E. Good, and N. F. Good. 1979. Production dynamics for above and belowground components
of a New Jersey Spartina alternifloratidal marsh. Estuarine and Coastal Marine Science 9:189-201.
Spain, A. V., and J. A. Holt. 1980. The elemental status of the foliage and branch-wood of seven mangrove
species from northern Queensland. 0643027408 :, CSIRO, [Melbourne].
Steinke, T., C. Ward, and A. Rajh. 1995. Forest structure and biomass of mangroves in the Mgeni estuary, South
Africa. Hydrobiologia 295:159-166.
Suzuki, E., and H. Tagawa. 1983. Biomass of a mangrove forest and a sedge marsh on Ishigaki Island, south
Japan. Japanese Journal of Ecology 33:231-234.
Tam, N., Y. Wong, C. Lan, and G. Chen. 1995. Community structure and standing crop biomass of a mangrove
forest in Futian Nature Reserve, Shenzhen, China. Hydrobiologia 295:193-201.
Tamai, S., R. Tabuchi, K. Ogino, and T. Nakasuga. 1986. Standing biomass of mangrove forests in southern
Thailand. Journal of the Japanese Forestry Society 68:384-388.
Terrados, J., and J. Ros. 1992. Growth and primary production of Cymodocea nodosa (Ucria) Ascherson in a
Mediterranean coastal lagoon: the Mar Menor (SE Spain). Aquatic botany 43:63-74.
Thant, Y. M., and M. Kanzaki. 2011. Biomass and carbon sequestration in community mangrove plantations anda natural regeneration stand in the Ayeyarwady Delta, Myanmar. American Geophysical Union Fall Meeting
2011.
Thiéry, A., and L. Puente. 2002. Crustacean assemblage and environmental characteristics of a man-made solar
saltwork in southern France, with emphasis on anostracan (Branchiopoda) population dynamics. Hydrobiologia
486:191-200.

DRAFT 2013 Wetlands Supplement

4.45

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878

Townsend, E. C., and M. S. Fonseca. 1998. Bioturbation as a potential mechanism influencing spatial
heterogeneity of North Carolina seagrass beds. Marine Ecology Progress Series 169:123-132.
Twilley, R., R. Chen, and T. Hargis. 1992. Carbon sinks in mangroves and their implications to carbon budget of
tropical coastal ecosystems. Water, Air, and Soil Pollution 64:265-288.
Twilley, R. W., A. E. Lugo, and C. Patterson-Zucca. 1986. Litter production and turnover in basin mangrove
forests in southwest Florida. Ecology 67:670-683.
Udy, J. W., and W. C. Dennison. 1997. Growth and physiological responses of three seagrass species toevaluated sediment nutrients in Moreton Bay, Australia. Journal of Experimental Marine Biology and Ecology
217:253-277.
Utrera-López, M. E., and P. Moreno-Casasola. 2008. Mangrove litter dynamics in la mancha lagoon, Veracruz,
México. Wetlands Ecology and Management 16:11-22.
van Houte-Howes, K., S. Turner, and C. Pilditch. 2004. Spatial differences in macroinvertebrate communities inintertidal seagrass habitats and unvegetated sediment in three New Zealand estuaries. Estuaries 27:945-957.
Van Lent, F., P. Nienhuis, and J. Verschuure. 1991. Production and biomass of the seagrasses Zostera noltii
Hornem. and Cymodecea nodosa (Ucria) Aschers. at the Banc d'Arguin (Mauritania, NW Africa): a
preliminary approach. Aquatic Botany 41:353-367.
van Tussenbroek, B. I. 1998. Above-and below-ground biomass and production by Thalassia testudinum in a
tropical reef lagoon. Aquatic botany 61:69-82.
Valiela, I., E. Kinney, J. Culbertson, E. Peacock, and S. Smith. 2009. 4. Global losses of mangroves and salt
marshes. In Global Loss of Coastal Habitats: rates, causes and consequences, Duarte, C., ed., Fundacion BBVA,
ISBN: 978-84-96515-84-0.
Vegas-Vilarrúbia, T., F. Baritto, P. López, G. Meleán, M. E. Ponce, L. Mora, and O. Gómez. 2010. TropicalHistosols of the lower Orinoco Delta, features and preliminary quantification of their carbon storage. Geoderma
155:280-288.
Vermaat, J., N. Agawin, C. Duarte, M. Fortes, N. Marba, and J. Uri. 1995. Meadow maintenance, growth andproductivity of a mixed Philippine seagrass bed. Marine ecology progress series. Oldendorf 124:215-225.
Vermaat, J., J. Beijer, R. Gijlstra, M. Hootsmans, C. Philippart, N. Van den Brink, and W. Van Vierssen. 1993.
Leaf dynamics and standing stocks of intertidal Zostera noltii Hornem. and Cymodocea nodosa (Ucria)
Ascherson on the Banc d’Arguin (Mauritania). Hydrobiologia 258:59-72.
Walker, D. 1985. Correlations between salinity and growth of the seagrass Amphibolis Antarctica (labill.)
Sonder & Aschers., In Shark Bay, Western Australia, using a new method for measuring production rate.
Aquatic botany 23:13-26.
Walters, B. B., P. Rönnbäck, J. M. Kovacs, B. Crona, S. A. Hussain, R. Badola, J. H. Primavera, E. Barbier, and
F. Dahdouh-Guebas. 2008. Ethnobiology, socio-economics and management of mangrove forests: a review.
Aquatic Botany 89:220-236.
Wang, J.-K. 1990. Managing shrimp pond water to reduce discharge problems. Aquacultural engineering 9:6173.
West, R., and A. Larkum. 1979. Leaf productivity of the seagrass, Posidonia australis, in eastern Australianwaters. Aquatic Botany 7:57-65.
Whigham, D. F., J. McCormick, R. E. Good, and R. L. Simpson. 1978. Biomass and primary production in
freshwater tidal wetlands of the middle Atlantic coast. Academic Press, New York.
Williams, S. L. 1987. Thalassia testudinum and Syringodium filiforme in a Caribbean lagoon. Mar Ecol. Prog.
Ser 35:91-98.
Woodroffe, C. D., and T. J. Moss. 1984. Litter fall beneath Rhizophora stylosa Griff., Vaitupu, Tuvalu, South
Pacific. Aquatic botany 18:249-255.
World Bank 2006. Aquaculture: Changing the Face of the Waters Meeting the Promise and Challenge of
Sustainable Aquaculture.
Yarbro, L. A., and P. R. Carlson Jr. 2008. Community oxygen and nutrient fluxes in seagrass beds of Florida
Bay, USA. Estuaries and coasts 31:877-897.
Yu, O., and G. Chmura. 2009. Soil carbon may be maintained under grazing in a St Lawrence Estuary tidal
marsh. Environmental Conservation 36:312-320.

4.46

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1879
1880
1881
1882

Zhang, J.-P., C.-D. Shen, H. Ren, J. Wand, and W.-D. Han. 2012. Estimating change in sedimentary organiccarbon content during mangrove restoration in Southern China using carbon isotopic measurements.
Pedosphere 22:58-66.

1883
1884
1885
1886

DRAFT 2013 Wetlands Supplement

4.47

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft
1887

Annex 4A.1 Salinity-based definitions

1888
Commondescription

Salinity (ppt)1

Tidal fresh water

0.5-5

Brackish water

5 - 30

Saline water>30

1
ppt is parts per thousand (‰) and is roughly
equivalent to grams of salt per litre of water.

1889
1890

1892

Annex 4A.2 Estimation of aboveground mangrove biomass:
higher tier methodology

1893
1894
1895
1896
1897
1898
1899

Because of field conditions and heavy weight of wood, an accurate survey of a mangrove forest is difficult andtime-consuming. Allometric methods (Soares and Schaeffer-Novelli, 2005; Komiyama et al., 2008) estimate thewhole or partial weight of a tree from measurable tree dimensions, notably trunk diameter and height, using
allometric relations developed from empirical measurement of weight of individual tree components (leaves,branches, stem). Use of allometric equations is favored because it is non-destructive and is therefore useful for
estimating temporal changes in forest biomass by means of subsequent stem diameter measurements oversubsequent years.

1900
1901
1902
1903
1904
1905

Up until recently, the major drawback of this method has been the site- and species-specific differences inallometric relations, necessitating the use of different allometric equations for different sites (e.g., Smith andWhelan, 2005) and, at a minimum, different species. However, a number of workers, using global datasets, havedeveloped a common allometric equation applicable for all tropical tree species, with the most applicable
equations for aboveground biomass being those developed for all tropical trees by Chave et al. (2005) and for all
mangrove species by Komiyama et al. (2005):

1906

Wtop = 0.168pDBH2.47 (Chave et al. 2005)

1907

Wtop = 0.251pD2.46 (Komiyama et al. 2005)

1908
1909
1910
1911
1912
1913
1914

where Wtop = aboveground tree weight in kg DW; D = tree diameter; DBH = diameter-at-breast height. The
relative error of each equation varies among species, but is typically within the range of -10% to +10%. There
are, of course, arguments to be made that empirical measurements should be made in all mangrove forests,
considering the significant allometric differences between species and for the same species at different locations(Smith and Whelan, 2005; Soares and Schaeffler-Novelli, 2005). However, this idea is impractical for inventory
compliers; a relative error of ± 10% is acceptable being within the range of error for allometric relations within aforest where biomass has been weighted.

1915
1916
1917
1918

Comparing the two equations, the Chave estimation gives lower aboveground weight estimates than that of the
Komiyama equation. Presuming that a complete census of all trees, with species identified, and their diameter
have been undertaken from replicate plots within a given forest, these numbers can then be used in either
equation to derive individual tree weight.

1891

1919

4.48

DRAFT 2013 Wetlands Supplement

DO NOT CITE OR QUOTE

Chapter 4: Coastal Wetlands
Final Draft

1920
1921

Annex 4A.3 Wood density of mangrove species

1922
Speciesn

Average density(tonnes m-3)

Standard error

Brugueria gymnorrhiza

8

0.81

0.07

Xylocarpus granatum

7

0.61

0.04

Sonneratia apetala
Sonneratia alba
Rhizophora mucronata
Rhizophora mangle

3
5
7
9

0.54
0.47
0.84
0.88

0.01
0.12
0.05
0.02

Rhizophora apiculata
Laguncularia racemosa
Heritiera littoralis

4
3
6

0.88
0.61
0.84

0.06
0.01
0.05

Heritiera fomes
Excoecaria agallocha
Ceriops tagal

2
7
8

0.96
0.41
0.85

0.14
0.02
0.04

Ceriops decandra

2

0.87

0.10

Avicennia officinalis

3

0.63

0.02

Avicennia marina

5

0.70

0.06

Avicennia germinans

6

0.76

0.04

Average
0.73
0.02
Source: Global Wood Density Database
http://datadryad.org/resource/doi:10.5061/dryad.234/1?show=full; Kauffman and Donato
2010, Bosire et al., 2012

1923
1924

Annex 4A.4 Percent refractory carbon

1925
1926
1927

Percent refractory carbon in organic/mineral soils were estimated for mangrove soils based on either the amountof phenolic compounds/lignins in soils or % TOC in mangrove soils deeper than 1 m if there was no further
decline in TOC concentration.

1928
1929
1930

PERCENT REFRACTORY CARBON APPLIED TO
ESTIMATE % C OXIDATION FOR MANGROVESOILS (% BY SOIL DRY WEIGHT)

1931
1932
1933

Mean

3.98

Median3.4

N

16

Prasad & Ramanathan 2009; Marchand et al.
2003;Dittmar & Lara 2001; Koch et al.2011; Ranjan etal.,2010; Marchand et al. 2005), which is similar to thatin tidal marshes (Filipet al. 1988; Alberts et al., 1988;
Ramesh et al. 2008)

DRAFT 2013 Wetlands Supplement

4.49

Chapter 4: Coastal Wetlands

DO NOT CITE OR QUOTE

Final Draft

1935

Appendix 4a.1: Future methodological development for
estimating C export

1936
1937
1938
1939
1940
1941
1942

The amount of dissolved and particulate carbon potentially available for export is highly variable among coastal
wetlands, depending on a large number of factors such as: net primary productivity, tidal range, the ratio of
wetland to watershed area, lateral trapping of tidal water, the presence of high salinity plugs in the tropical dry
season, total wetland area, frequency of storms, amount of precipitation, and volume of water exchange Eachecosystem is unique; some wetlands export DOC but import POC, others import DOC and POC but export DIC,
while other systems import or export all forms of dissolved and particulate carbon. The direction of net exchange
also usually varies within the same estuary with change in season.

1943
1944
1945
1946
1947
1948
1949
1950
1951
1952

Estimation of tidal exchange in a particular wetland is not a straightforward process. Many workers haveprovided rough estimates by multiplying carbon concentrations suspended in wetland creeks and waterways by
the tidal range multiplied by the creek/waterway cross-sectional area. Estimates derived from such simple
calculations are invalid and misleading for a number of reasons, including the inherent assumption that there aredifferences in carbon concentrations between ebb and flood tide stages and that the tidal prism is symmetrical. In
fact, carbon concentrations in many wetland waters do not show significant differences between tides. Further,tides in most wetlands are characterized by a pronounced asymmetry between ebb and flood tides with the ebb
most often being of shorter duration but with stronger current velocity than the flood tide. Also, tidal velocities
vary across a waterway with faster surface current velocities mid-stream than those just above the creekbed or
proximal to the wetland.

1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965

For these reasons, it is not possible to make simple generalizations regarding total carbon export from mangroves,seagrasses or tidal marshes and, in fact, comparatively few such measurements have been made properly. The
correct method would be to measure water volume and velocity over entire tidal cycles over several seasons inrelation to position in the water-column to derive an overall annual estimate of average water flow by volume.
This involves fairly complex instrument measurements and sophisticated mathematical modelling as well as
extensive and expensive repetitive measurements of dissolved and particulate carbon concentrations. For
mangroves, net exchange of carbon has been properly measured in only twelve systems, with no clear exchange
patterns among locations, although it does appear that most mangroves export POC as litter but with rates
ranging widely from 0.1-27.7mol C m-2 yr-1(Alongi, 2009). This export equates globally to only about 10% of
total carbon fixed by trees; respiration to the atmosphere is by far the largest loss of C to the atmosphere. Suchappears to be the case for tidal marshes (Chmura et al., 1993) and subtidal seagrass beds (Fourqurean et al.,2012). Some recent syntheses and literature do hold promise for future development of model relationships thatcan be used for estimating C export (Adame and Lovelock 2011; Maher et al., 2013).

1934

1966

4.50

DRAFT 2013 Wetlands Supplement