Wetlands cover only about 1% of the Earth's surface, yet are responsible for a much greater proportion of biogeochemical fluxes between the land surface, the atmosphere and hydrologic systems. They play a particularly important function in processing methane, carbon dioxide, nitrogen, and sulfur as well as in sequestering carbon. Considerable progress has been made in the past 10 years regarding wetlands and methane: a global digital dataset of wetlands has been produced, global observations of methane have been combined with global three-dimensional atmospheric modelling to constrain modelled fluxes of methane from high-latitude wetlands. Furthermore, significant advances have been made in understanding the biogeochemical processes that control fluxes of methane and other trace gases. Progress to date suggests that current wetland classification schemes clearly do not accurately reflect their functional roles because they have been based on wetland attributes such as dominant plant types which do not reflect differences in the biogeochemical cycles of wetlands. Further, traditional wetland classifications cannot be distinguished on the basis of global remotely sensed observations. Consequently, it has been impossible to accurately quantify the distribution of key fluxes on the basis of observed land cover.

GAIM, in conjunction with BAHC, LUCC, IGAC, and DIS, held a joint workshop for the purpose of advancing the state of knowledge regarding the distribution of wetlands as well as developing a global functional wetland characterization scheme. The purpose of the workshop was to establish an effective functional parameterization of wetlands directed toward integrating wetland trace gas, hydrologic, and nutrient fluxes into regional and global biogeochemical models (Fig. 7.1). In the past, wetlands have been classified in various ways on the basis of hydrology, geomorphology, and vegetation. However, for the purpose of understanding the effects of wetlands on global biogeochemical cycles, it was necessary to devise a functional characterization of wetlands, so that wetland distributions could be included in global biogeochemical models.

Figure 7.1: Linking observations from field and remotely sensed data to provide the necessary biological and chemical information to estimate fluxes of CH₄, CO₂, N, and S from wetlands.

Wetlands have been defined in various ways. At the workshop, a working definition of wetlands was developed as "An area in which a) the water table is at or near the soil surface for a significant part of the growing season; and b) soils are covered by active vegetation (during the period of water saturation)." In the past, wetlands have been classified in various ways on the basis of hydrology, geomorphology, and vegetation. However, for the purpose of understanding the effects of wetlands on global biogeochemical cycles, it is necessary to devise a functional characterization of wetlands, so that distributions of wetlands can be included on this basis into global biogeochemical models. The primary missions of the IGBP Wetlands Workshop was to develop a scheme for functional characterization of wetlands and to evaluate applications of remote sensing to studies of wetlands biogeochemistry.

The timing and extent of flooding is the key environmental factor controlling ecological processes in wetlands. Flooding brings nutrients and creates the physical environment required by the plants and microbes. Hence, modifications to wetland hydrology severely disrupt their function. The U.S. and Europe have drained and converted wetlands extensively. Land use changes in developing countries are increasingly eliminating wetlands on a global basis. Moreover, given the expected increase in human population (mostly in developing regions) the pressure to convert wetlands for agriculture to meet growing food requirements is expected to increase even further.

The extent of wetlands is uncertain because it is difficult to identify and classify wetlands on a global scale. In addition, the aerial extent of wetlands is modified as a result of land-use changes, so that once a globally consistent classification scheme is established, the aerial distribution must be monitored and recompiled periodically. The global extent of wetlands has been estimated as 5.3x10¹² m² to 8.6x10¹² m², however, these figures are uncertain. While relatively small compared to ocean, savanna, or forest area, wetlands are biogeochemically active because of their high productivity and redox gradients. In particular, wetlands are major natural sources of reduced gases such as methane and sulfur compounds, and can have high rates of denitrification and nitrogen fixation.

Current estimates of the global extent of wetlands involve either the compilation of anecdotal information from interviews or questionnaires or more systematic planimetry of areas identified as swamps on worldwide operational navigation charts. In the late 1980s, more complete inventories became available for North America. Recent mapping of wetlands is driven by national agencies, with non-governmental organizations generally ahead of national governments in extending the identification of wetlands. There is not yet any global assessment of the seasonal variation in the extent of wetlands. Furthermore, remote sensing has not been systematically applied to the problem of identifying the global extent and distribution of wetlands.

The national inventories are usually conducted for resource management or conservation, rather than for global change research, and consequently they identify only the portion of wetlands that are occupied by some particular type of surface such as wildfowl habitat. Thus, to greater or lesser degree they (and the aforementioned regional surveys) underestimate the inundatable area that is of interest to global biogeochemists. Thus, there is a need to intensify national wetland inventories in various parts of the world and to integrate them into regional and global calculations.

It should be emphasized that such typical inventories include extent and (regional) position only. There is a large gap between such an inventory of extent and an inventory of the variables which have been determined to be required for functional modeling of wetland production (e.g. methane production, carbon storage, etc.). For these purposes, parametric data (e.g. temperature, net primary productivity (NPP), soils, etc.) will need to be combined with information on the spatial extent of wetlands around the globe.

Wetland Functional Parameterization

There are several major biogeochemical constituents which are controlled by processes which occur in wetlands. These materials include carbon dioxide (CO₂), methane (CH₄), nitrogen, dimethyl sulfide (DMS), sulfur, and reducing agents such as iron (Fe³⁺). Of these, four were chosen as the basis for developing a functional classification for wetlands: nitrogen, sulfur, iron, and carbon (CH₄, CO₂). The flux of each was determined by several processes which act in all wetland ecosystems to varying degrees.

The challenge to the wetlands research community was to develop algorithms that relate the 9 input parameters to the 4 wetland functions (Table 7.1) such that a model could be constructed to enable predictions, and to test the model by independent, direct observations. There were various types of measurable data necessary to assess each of the nine functional parameters. The most important of these are highlighted below. (Within each, the most critical measurements are in bold italic.)

NEP- Biomass (above & below ground), litterfall (leaf & wood), PAR , Soil/water respiration

Temperature (atmospheric), wind speed, relative humidity

Hydrology- Flow, position of water surface, periodicity (tides, seasonal, etc.), aerial extent, phase (solid, liquid), Precipitation, Evapotranspiration, infiltration (and subsurface flow)

Organic Transport- Grazing, harvesting, fire, waterborne processes, airborne processes, decomposition, dry deposition, erosion

Vegetation- Functional groups (e.g. periplankton, phytoplankton, hydrophytes, shrubs, herbs, trees, sedges, grasses, bryophytes, legumes), morphology or physiognomy, phenology

Water Chemistry- Nutrients (N,C,S), dissolved Oxygen (w/ water temp), redox potential, metals (Fe, Mn), temperature

Salinity or conductivity (w/ water temperature)

Soils- Texture, nutrient status (C,N,S,P,K), organic content, moisture, depth of bacteriologically active soil, cation exchange

Geomorphology- Channel and basin size, distribution, form, slope

Understanding Wetland Processes: A Set of Research Priorities

The functional parameterization described above was based on parameters whose values could be quantifiable, either by direct measurement, proxy, or modelling. With values for each parameter, it was possible to assign a position in parametric 9-space for an individual wetland. The most significant impediments to quantitative assessment wetland functionality are:

A. Wetland extent & distribution (classification and definition)
The information base is inadequate (i.e. missing data, poor data, poorly disseminated data sets). Compilations have been constrained by lack of agreement on definition and classification. There is a need to compare and relate the functional parameterization to widely used biodiversity or conservation oriented classifications which are usually hierarchical.

B. Uneven spatial and temporal data coverage
Available information is biased to the northern boreal and temperate zones, being generally sparser for tropical and southern subtropical and temperate zones. In addition, spatial and temporal aspects need further investigation (e.g. periodicity of inundation: permanent, seasonal, intermittent, episodic).

C. Variable data availability and quality for various constituents (methane, CO₂, etc.)
Primary data requirements are generally the same for all biogeochemical processes being considered, but information is not uniformly available.

D. Soils information
Global compilations of soils information are generally poor or misleading from the standpoint of wetland functionality.

E. Hydrological data Hydrological data are poor except for a few very well-studied wetland sites.

F. Anthropogenic influences The history of anthropogenic influences through land-use changes, water works and other activities are not well quantified. This should be incorporated as part of a general 200 year land use data base.

In light of the above gaps in information and understanding, a set of research priorities was developed as follows:

1. Wetland extent- The largest gap in wetland characterization is the size of wetlands themselves, both in space and time. The level of flooding and the areal extent of wetlands is the largest uncertainty in applying models of wetland function to models of the global system. Both the temporal and areal extent of wetland flooding should be characterized in terms of ha-days. An additional factor is the phasing of flooding (i.e. continuous or intermittent). These issues are not adequately addressed in present land cover compilations and terrestrial ecosystem models.

2. Soil characterization- Existing data bases should be assessed for adequacy regarding wetland soils. Critical aspects are organic content and texture (sand, silt and clay content).

3. Correlative studies- Most models run on correlations so additional studies are required to better define the relationships between the processes of interest and the input parameters. Correlations which make use of variables which can be remotely sensed are the most useful. For example, Bubier et al., [1995] explored the correlation between vegetation and methane emission. Tree and sedge cover were good indicators of low and high flux, respectively. Bryophytes also are generally indicative of low emissions but may release DMS.

4. Mechanistic Studies- A detailed understanding of wetland systems is necessary in addition to field measurements for model validation and more data to correlate the nine parameters to the four functions. Mechanistic studies are required so that the correlations described above are not misapplied. For example, Q₁₀ published for methane to temperature ranges from 1.6 to 20. However Q₁₀ values greater than 3 or 4 are probably not physiologically meaningful in terms of microbial physiology. Most likely the high observed Q₁₀ correlations are due to simultaneous changes in temperature and substrate availability [Whiting and Chanton, 1993] and changes in substrate availability coincident with temperature increase are being mistaken for a temperature response. Another example is the relationship between NPP and CH₄ emissions. Mechanistic studies are required to reveal the details of this correlation which could be used to enable prediction of the timing of the relationship between these parameters. If NPP increased in one year, would methane emission increase in the same year, or some number of years later? Additionally, mechanistic studies will yield understanding to allow for hypothesis development in terms of the response of wetlands to changing climatic conditions [Dacey et al., 1994].

NPP is estimated by many ecosystem models. However, the NPP of wetlands is an important function upon which the functional classification is based. Consequently, it will not be possible to use the gross output of ecosystem models to calculate NPP within wetlands for the purpose of wetland classification. Rather, it will be necessary to incorporate wetland processes within the larger ecosystem models and simultaneously calculate NPP for wetlands (for classification subroutines) and develop wetland functional distribution for the purpose of biogeochemical fluxes.

Isotopic studies (e.g. ¹³C, ¹⁴C, ¹⁵N and ³⁴S) are useful for elucidating the mechanisms of the biogeochemical cycles. For example, ¹³C studies of soil organic matter are necessary to elucidate carbon cycling in C-3 and C-4 plants. These studies have been useful for determining the effects of land use change from forest to grassland or forest to pasture [Trumbore et al., 1995]. Additionally, methane ¹³C increases by 15ä going down the Amazon basin, possibly due to the increase in C-4 plants [Devol et al., 1990]. The C-3 or C-4 nature of the original plant material can have a dramatic effect on methane carbon isotopic composition [Chanton and Smith, 1993].

5. Vegetation Scheme- Various systems for organizing vegetation should be evaluated for their relationships to the functions of interest and for their accessibility to determination by remote sensing. Categorization might vary between woody (shrub or tree) or non woody (sedge) or non-vascular (phytoplankton or moss). This scheme should be refined and compared to other floristic schemes. This may help test if the functional groups used are correct as well as the relative extent of the different functional groups.

6. Wetlands Data- The highest data priorities related to the above information were identified as: A. Wetland Inventory (underpinned by a suitable parameterized classification), with emphasis on bolstering tropical and southern hemisphere data. B. Hydrological data C. Soils

7. Model Development- A comprehensive model needs to be developed that accommodates all types of wetlands, including rice fields and natural wetlands, bogs, fens, flooded forest, marsh, etc. Workshop participants formulated a nine-parameter functional n-space into which all wetlands can be plotted. The formulation was directed jointly by field ecologists and scientists with remote sensing expertise to define functions and determine the types of data sets which could be brought to bear on the problem of discrimination between wetlands with different sets of parametric values. The result was a 9-dimensional model with a descriptive component and a process component. The initial nine parameters proposed by workshop participants were: hydrology, temperature, primary production, vegetation, soil, salinity, water chemistry, transport of organics and sediment, and topography/geomorphology. The nine primary deterministic functions were formulated to represent the driving biogeochemical cycles and the minimum number of necessary observational schemes in wetland ecosystems. While the nine parameters were not completely independent of one another, they could be measured and described with existing techniques and data. The relationship between the nine functional parameters (j) and important wetland processes (i) (e.g. methanogenesis, carbon accumulation, etc.) was formulated as

This can be represented by a 9 dimensional graph with axes defined by the nine functional parameters, on which any wetland ecosystem corresponds to a certain 9 dimensional volume. Any wetland that falls within that volume has the same functional characteristics, and can be mapped and inventoried accordingly. This is analogous to principal component or cluster analysis. A flattened visualization of this for two contrasting wetlands is illustrated in Figure 7.2.

The nine primary deterministic functions were formulated to represent the driving biogeochemical cycles and the minimum number of necessary observational schemes in wetland ecosystems. While the nine parameters were not completely independent of one another, they could be measured and described with existing techniques and data. Any wetland that fell within a given volume had identical functional characteristics, and could then be mapped and inventoried accordingly. This technique was analogous to principal component or cluster analysis.

Figure 7.2: Graphical representation (2-D) of 9-dimensional parameter space for wetland parameterization scheme. Two wetland functional types are shown. Type A has high salinity, high proportion of vascular plants, high temperature, etc. Type B has low salinity, high gradient in surrounding regions, high soil carbon & nutrient content, etc. Any wetlands with similar shapes on this 9-dimensional representation are postulated to have the same set of functional processes (controlling CH₄, CO₂, N, S) regardless of where they are found. However, wetlands with different shapes on this representation can also share the same functions with appropriate trade-offs between the various parameters.

Rather than a build series of models for a number of wetland categories, a single conceptual model was developed to encompass wetland function and category. The model contained an interactive classification scheme where functionally based modeled or measured input parameters could be queried which displayed the wetland characteristics as contours. If, for example, one is interested in methane, the interaction of the input parameters could yield methane emission contours.

One of the primary purposes of developing a wetland functional parameterization scheme is to be better able to constrain the role of wetlands in the global biogeochemical system as modelled by terrestrial ecosystem models of various types. Such models can use a wetland functional class as a refinement of an input biome or vegetation type. Whereas there is no biome distribution scheme which is universally adopted by all modelling groups, the wetlands functional characterization scheme has provided a common subset of data for this particularly important source and flux of methane and other biogeochemically active materials. In addition, it will be important to incorporate wetland function into terrestrial ecosystem models to better capture the wetland internal functions and their interactions with the larger terrestrial ecosystem.