Approaches to Scaling of Trace Gas Fluxes in Ecosystems potx

The overall goal of the workshop was to investigate approaches to reduce uncertainties in estimates of fluxes of trace gases and aerosols between terrestrial and aquatic ecosystems and t

vii

F O R E W O R D

The world's terrestrial and aquatic ecosystems are important sources of a number of greenhouse gases and aerosols which cause atmospheric pollution and disturb the energy balance of the Earth-atmosphere system In recent decades the measurement techniques and instrumentation for quantifying gas fluxes have been improved considerably Yet, the uncertainties in the regional and global budgets for a number of atmospheric compounds have not been reduced due to the large spatial heterogeneity and temporal variability of the factors that control gaseous fluxes in ecosystems

Techniques used for extrapolating measurements or properties and constraining results between different temporal and spatial scales are nowadays referred to as "scaling" All scaling methods are embedded in the data Apart from uncertainties associated with the data used, errors may be caused by generalization of the basic data (e.g in soil maps, ocean maps) Moreover, much of the spatial and temporal variation at a detailed level is obscured as a result

of aggregation Possible errors caused by the use of aggregated or generalized data in models are generally not explicitly analyzed

An important step in scaling of gas exchanges between ecosystems and the atmosphere is the delineation of functional types where distinct differences in structure, composition or properties of landscapes or water bodies coincide with functions or processes relevant for gas fluxes Delineation reduces the variability of state variables, and therefore functional types form a good basis for measurement strategies and model development

Models are widely used tools in bottom-up scaling approaches Models can also be used to calculate flux values for regions where less intensive or no measurement data are available One of the challenges in model development is the integration of properties or variables in space and time, accounting for the spatial and temporal variability of processes involved in gas production, consumption and exchange

Scaling not only comprises bottom-up approaches, but also top-down methods, such as inverse modelling to calculate from the atmospheric concentrations back to the sources Top- down scaling in general involves the validation of estimates obtained at a lower scale level against constraints given at a higher level of scale Hence, scaling requires uncertainty analysis

at all levels considered

The present book is a collective effort of a diverse group of scientists to review the state- of-the art in the field of scaling of fluxes of greenhouse gases and ozone and aerosol precursors It focuses on identification of gaps in knowledge, and on finding solutions and determining future research directions The book is the result of an international workshop on

"Scaling of trace gas fluxes between terrestrial and aquatic ecosystems and the atmosphere", held from 18-22 January 1998 at kasteel "Hoekelum", Bennekom, the Netherlands The workshop was organized by the International Soil Reference and Information Centre (ISRIC)

as a follow-up to the international conference on "Soils and the Greenhouse Effect" which ISRIC organized in 1989

The overall goal of the workshop was to investigate approaches to reduce uncertainties in estimates of fluxes of trace gases and aerosols between terrestrial and aquatic ecosystems and the atmosphere at the landscape, regional and global scale To achieve that goal, the participants concentrated on: (i) Identification of data gaps in scaling approaches between

viii

field, landscape, regional and global scales; (ii) Development of procedures to bridge process level information between different scales; (iii) Assessment of methods for integration, aggregation and other data operations; and, (iv) Assessment of approaches to uncertainty analysis in bottom-up and top-down scaling

The workshop was one of researchers with many different backgrounds, including soil science, microbiology, oceanography, rec.ote sensing and atmospheric sciences The group included experts in the determination of gas fluxes, modellers, specialists in the use of isotopes and tracers, and researchers working on the compilation of regional and global inventories and maps of soils, vegetation, land use and emissions

Twelve invited background papers, providing a review of the field, were distributed prior to the workshop, but were not presented at the meeting Instead, the scientific programme of the workshop consisted of five days of discussions according to the well-known Dahlem workshop model The participants were divided in four interdisciplinary working groups which met to address the workshop aims and give concise and practical recommendations, concentrating on the following questions: (i) How can fluxes of trace gas species be validated between different scales ?; (ii) How can we best define functional types and integrate state variables and properties in time and space ?; (iii) What is the relation between scale, the model approach and the model parameters selected ?; (iv) How should the uncertainties in the results of scaling be investigated ? The four group reports are included in this volume as separate chapters together with the peer-reviewed background papers

The organizing committee for the workshop, which started discussions Jn 1996, included the following members: A.F Bouwman (National Institute of Public Health and the Environment, Bilthoven), N.H Batjes (International Soil Reference and Information Centre, Wageningen), H.A.C Denier van der Gon (Soil Science and Geology Department, Wageningen Agricultural University), F.J Dentener (Institute for Marine and Atmospheric Research, Utrecht University), J Duyzer (TNO Institute of Environmental Sciences, Energy Research and Process Innovation, Apeldoorn), W Helder (Netherlands Institute for Sea Research, Den Burg),

J Middelburg (Netherlands Institute of Ecology, Centre for Estuarine and Coastal Ecology, Yerseke)

The organization of the workshop was made possible through funds of the Commission of the European Communities (CEC-DG XII), European IGAC Office (EIPO), International Fertilizer Industry Association (IFA), Kemira Agro Oy, National Institute of Public Health and the Environment (RIVM), Norsk Hydro, Netherlands Royal Academy of Arts and Sciences (KNAW), Shell Nederland b.v., and the Netherlands Organization for Applied Scientific Research (TNO)

Cooperating organizations were the Intemational Society of Soil Science (ISSS), International Geosphere-Biosphere Programme (IGBP), International Global Atmospheric Chemistry Programme (IGAC), Global Emission Inventories Activity (GEIA), Centre for Climate Research (CKO), and the Climate Change and Biosphere Programme of the Wageningen Agricultural University (CCB)

Dr L.R Oldeman

Director, Intemational Soil Reference and Information Centre (ISRIC)

October 1998

ix

ACKNOWLEDGEMENTS

This volume is the result of an international workshop on "Scaling of trace gas fluxes between terrestrial and aquatic ecosystems and the atmosphere", held from 18-22 January 1998 at kasteel "Hoekelum", Bennekom, the Netherlands It is a collective effort of a diverse group of scientists The choice of topics, identification of authors for the invited background papers, and the scientific programme around four key questions is the result of discussions in the organizing committee Thanks are due to the members of this committee, Niels Batjes, Hugo Denier van der Gon, Frank Dentener, Jan Duyzer, Wim Helder and Jack Middelburg Thanks to the enthusiastic involvement of the committee members the workshop became a very success- ful one

I wish to thank the chairmen and rapporteurs of the four working groups for leading the discussions and summarizing the various contributions of the working group members in four reports which are included in this book: Andi Andreae and Willem Asman (group I), Jean- Paul Malingreau and Sybil Seitzinger (group 2), Peter Liss and Jack Middelburg (group 3), Arvin Mosier and Dick Derwent (group 4)

I am indebted to all participants for reviewing the invited background papers Carl Brenninkmeier, who could not attend the meeting, was so kind to provide a review of one of the papers I am also thankful to Niels Batjes for critically reading two papers, and to Ruth de Wijs-Christensen of RIVM for editing one of the background papers

I very much appreciated the support and ideas of Roel Oldeman Hans van Baren of ISRIC during the preparation of the workshop Special thanks are due to Jan Brussen, Yolanda Karpes-Liem and Hans Berendsen for their enthusiasm and input during the preparations of the workshop and the workshop Finally, I am grateful to Wouter Bomer for designing the workshop logo (also presented on the cover of this book) and for preparing some of the figures in the book

Finally, I wish to thank Fred Langeweg of RIVM for giving me the opportunity to work on this projectl

Lex Bouwman, editor

October 1998

A.F Bouwman, editor

9 Elsevier Science B.V All rights reserved

TOWARDS RELIABLE GLOBAL BOTTOM-UP ESTIMATES OF TEMPORAL

AND SPATIAL PATTERNS OF EMISSIONS OF TRACE GASES AND AEROSOLS

A.F Bouwman l, R.G Derwent 2 and F.J Dentener 3

~ National Institute of Public Health and the Environment, P.O Box 1, 3720 BA Bilthoven, The Netherlands

2 Meteorological Office, London Road, Bracknell, RG 12 2SZ Berkshire, UK

3 University of Utrecht, Institute for Marine and Atmospheric Research, Princetonplein 5, 3584 CC Utrecht, The Netherlands

I I n t r o d u c t i o n

Emission inventories play a dual role in global air pollution issues Firstly, they can be used directly to establish the more important source categories, to identify trends in emissions and

to examine the impact of different policy approaches Secondly, emission inventories are used

to drive atmospheric models applied to assess the environmental consequences of changing trace gas emissions and concentrations and to provide advice to policy makers This second role contributes to the atmospheric modelling community being an important user of emission inventories The assessment process for global air pollution problems has a number of identifiable steps: (i) it quantifies the changes in trace gas composition of the atmosphere; (ii)

it quantifies changes in atmospheric chemistry, transport, deposition and radiative forcing; (iii) it identifies the climate responses to the changes in atmospheric composition of the radiatively active trace gases; and, (iv) it quantifies the biological and ecological responses to the predicted changes in climate

The atmospheric modelling community will need a hierarchy of emission inventories to complete an assessment of global air pollution problems based on these steps over the next decade or so In their simplest form, atmospheric models merely require no more than fixed global emission fields of each relevant species However, in their most complex form, future atmospheric models will require emission fields whose spatial patterns and magnitudes will respond in a wholly self-consistent manner to changes in economic prosperity, demography, land use, climate change and policy The requirements placed on the emission inventories will change from the provision of fixed fields to the implementation of emission algorithms within the modelling system Gridded emission fields may slowly change from being the essential input to being the output of the modelling system

Alongside this anticipated increase in complexity in moving towards a process-based approach to emission inventories, there is a growing interest in the emissions of a wider range

of species For example, in climate change research at the start of the Intergovernmental Panel

on Climate Change (IPCC) process, assessment work was performed with present-day and doubled atmospheric carbon dioxide (CO2) concentrations This "steady state" or

"equilibrium" approach has now been replaced by the transient scenario approach in which CO2 concentrations increase with time in response to emission projections and carbon cycle modelling Further scenarios have been added to deal with the other major radiatively active trace gases: methane nitrous oxide (N20), tropospheric ozone (03), stratospheric ozone

IMARU Eulerian 3.75%3.75% 19 levels 1/2-hourly CH4 and NO• 7

chemistry KFA/GISS Eulerian 10%8% 15 levels 8-hourly Simplified 8 KNMI/CTMK Eulerian 2.5%2.5~ 15 levels 12-hourly 13 species 9 MCT/UiB Eulerian 150 kmx 150 kmx 10 levels hourly 51 species 10 MOGUNTIA Eulerian 10~ 10% 10 levels monthly CH4 and NOx 11

chemistry MOZART Eulerian 2.8%2.8% 18 levels 6-hourly 45 species 12 RGLK Eulerian 10~ 10% 0 levels Monthly SO2, NOx and NH3 13

chemistry STOCHEM Lagrangian 3.75~215 19 ' ~els 6-hourly 70 seecies 14 UiO/GISS Eulerian 8~ 10~ levels 8-hourly to 5-day 50 species 15

a 1, Li and Chang (1996); 2, Allen et al (1996); 3, Moxim et al (1996); 4, Chin et al (1996); 5, Penner et al (1994); 6, Mt~ller and Brasseur (1995); 7, Roelofs and Lelieveld (1995); 8, Kraus et al (1996); 9, Wauben et al (1997); 10, Flatoy and Hov (1996); 11, Dentener and Crutzen (1993); 12, Brasseur et al (1997); 13, Rodhe et al (1995); 14, Collins et al (1997);

15, Berntsen et al (1996)

and chlorofluorocarbons (CFCs) More recently, sulphur dioxide (SO2), dimethylsulphide (DMS), ammonia (NH3) and other aerosol species have been incorporated into the scenario approach to take into account the climate consequences of man-made fuel combustion and biomass burning There has therefore been an increasing interest in the details of the emission inventories of a wider range of trace gases and aerosols

Emission inventories are implemented in current atmospheric models to represent the processes by which trace gases and aerosols are discharged into the model atmosphere The models, commonly three-dimensional chemistry transport models (CTMs), then simulate the dispersion, diffusion and advection of t h i material away from its source r~'gions in response

to a continuously varying turbulent and chaotic atmospheric flow At some point, the material may be removed from the atmospheric circulation by dry or wet deposition or uptake in the oceans or it may undergo chemical transformation An immense amount of meteorological, chemical and process information is required to drive current CTMs This information can be made available from archived meteorological analyses or from the global climate models (GCM) used to predict future climate change The CTM may be incorporated within the GCM, in which case the atmospheric model is "on-line"; alternatively, the model is referred to

as "off-line" if the GCM and CTM are separated By way of example, details of the time and spatial resolution of 15 of the current CTMs are provided in Table 1 At present some 20 CTMs are being used to assess global air pollution problems Currently, CTMs use emission inventories for the trace gases and aerosol species listed in Table 2 Each emission is usually subdivided into up to about 10 major source categories Most source categories have different spatial grids applied and work with different seasonal and sometimes diurnal variations

We will focus here on the issues of "scaling" in the implementation of emission inventories

in current and future CTMs Scaling comprises all techniques used for extrapolating measurements or properties and constraining results between different temporal and spatial scales Very similar problems of scaling occur across various disciplines, such as ecology (Ehleringer and Field, 1993), soil science (Wagenet and Hutson, 1995; Hoosbeek and Bryant,

Towards reliable global estimates of emissions of trace gases and aerosols 5

Table 2 Some of the trace gases and aerosol species handled by current chemistry transport models (CTMs) for the assessment of global air pollution problems

HCFCs: 22, 141b, 142b Resuspended material

Perfluoro molecules: SF6, CF4, C2F6, C4F8 Biomass burning

Synthetic bromine compounds: 12B 1, 13B 1

1992) and global change research in general (O'Neill, 1988)

Two approaches are used for scaling gas fluxes: bottom-up and top-down scaling Bottom-

up approaches, calculated from smaller to larger scales, involve extending calculations from

an easily measured and reasonably well understood unit to more encompassing processes In bottom-up scaling, various problems occur, such as how to aggregate the spatial and temporal variation of properties or fluxes Other problems are the various data uncertainties involved, and translating mechanisms and processes between different scales

Top-down approaches can mean using the measurements at a higher scale level which set the boundary conditions for problem identification, and stimulate the testing of general relationships for specific cases (see Heimann and Kaminski, 1999) Examples of observations

at a higher level of scale that are used to constrain flux estimates include atmospheric concentrations and mixing-ratios of stable isotopes (see Trumbore, 1999) Comparison of the concentrations or deposition velocities simulated by transport models with observations can result in an expression of scientific confidence or a warning that crucial !r, formation is still missing Between these two extremes, top-down scaling can be very useful for testing hypotheses and identifying missing information

A number of methods exist to scale information, the most important being aggregation, generalization, stratification and modelling Aggregation involves the collection or uniting of information into an aggregate unit, generally by counting and addition Aggregated results can

be presented as the mean or median coupled with statistical information such as standard deviations

Generalization is the description of a group on the basis of properties of a sub-unit or member of the group considered to be representative, commonly based on expert knowledge This method is generally used when observational or statistical data on individual members of the group are scarce

The reverse action of aggregation, whereby the aggregate is subdivided into different components, may be the classification of a system into functional units with similar properties and environmental and management conditions that regulate trace gas fluxes (see Seitzinger et

al (1999) This is sometimes referred to as "stratification" (Matson et al (1989)

6 A.F Bouwman, R.G Derwent and F.J Dentener

Models break down a system into its main components and describe the behaviour of the system through the interaction of those components A discussion of the different types of models used can be found in Archer (1999) for aquatic systems and Schimel and Panikov (1999) for terrestrial systems

We will focus here on bottom-up scaling approaches for trace gas fluxes between aquatic and terrestrial ecosystems (including agroecosystems) and the atmosphere used in the develo- pment of global gridded emission inventories The discussion will be primarily on emission inventories prepared for scientific purposes such as atmospheric modelling Although our findings may also hold for other types of inventories, we will not discuss these inventories explicitly on the country or provincial (sub-national) scale Such inventories are now being prepared for non-scientific purposes (e.g national communications in the United Nations Framework Convention on Climate Change)

The first, and major, part of this paper discusses the uncertainties and problems of aggregation, generalization, stratification and modelling in the compilation of inventories Next, the available global emission inventories for land-use related and natural sources of trace gases will be discussed on the basis of their spatial and temporal resolution Finally, the spatial and temporal resolution of current CTMs will be confronted with the available emission inventories

2 Uncertainties in emission estimates

Among the various approaches to estimating fluxes, the major ones in use are the emission factor approach and modelling In emission factor approaches emission estimates are derived

by combining measurement data with geographic and statistical information on the ecosystem processes and economic activity This can be represented as:

where E is the emission, A the activity level (e.g area of a functional unit, animal population, fertilizer use, burning of biomass) and EU the emission factor (e.g the emission per unit of area, animal, unit of fertilizer applied or biomass burnt) When using the emission factor approach, both the stratification scheme for delineating functional types (e.g management systems, ecosystems, environmental provinces or entities) as a basis fol scaling, and the reliability of the emission factor determine the accuracy of the flux estimates

The firmest basis for scaling is developing an understanding of the mechanisms that regulate spatial and temporal patterns of processes, and describing these mechanisms in models Models are used to break down a system into its component parts and describe the behaviour of the system through their interaction In general, trace gas flux models include descriptions of the processes responsible for the cycling of carbon or nitrogen and the fluxes associated with these processes Various types of models exist, including regression models, empirical and process (or mechanistic) models

In the following sections the various sources of uncertainty in the estimates of emission rates for the emission factor approach, trace gas flux models and farm-scale models will be discussed, followed by uncertainties associated with the spatial and temporal distribution of the data underlying flux estimates We will not discuss uncertainties in the measurement data This problem will be examined in more detail by Lapitan et al (1999), Fowler (1999), Denmead et al (1999) and Sofiev (1999)

Towards reliable global estimates of emissions of trace gases and aerosols 7

2.1 U n c e r t a i n t i e s in t h e e m i s s i o n r a t e s

2.1.1 Emission f a c t o r approach

Uncertainty ranges for emission inventories are usually presented for the global total emission only, and not on a regional or grid-by-grid basis Uncertainties in emission inventories may be caused by uncertainties in the environmental and economic activity data used and in the measurement data themselves Uncertainties can also result from the lack of representative measurem'ents to resolve the full range of ,mvironmental conditions occurrhag in the systems considered and in the models used These sources of uncertainty will be discussed for the different approaches to flux estimation on the basis of a number of examples for different scales

- M e a s u r e m e n t data In a review of measurement data for biomass burning, Andreae (1991) proposed emission factors for several gas species Although the ranges in measured fluxes in field and laboratory experiments varied by more than a factor of 2 for most species

as a result of differences in fuel and burning conditions, one single emission factor was proposed for each gas species, representing the aggregated flux for smouldering and flaming fires for all fuel types (grass, wood, crop residues, etc.) For biomass burning it is difficult to delineate the types of fires and the different techniques used may introduce systematic differences, especially where reactive and difficult-to-measure species (such as NOx and NH3) are involved Clearly, one emission factor cannot describe all the burning conditions and fuel types

Another example illustrating the lack of measurement data concerns the emission coefficients used for animal housings in Europe In housings with mechan!cal ventilation the gas flux can be easily determined from the gas concentration in the ventilation air and the flow rate The trace gas emissions from naturally ventilated housings can only be determined indirectly and with greater uncertainty In such "open" housings the emission depends on the opening and closing of doors In large parts of Europe, housings for cattle - the most important category- are naturally ventilated (Asman, 1992) Besides being scant, the available measurement data need not be representative For example, the NH3 ammonia emission per animal may vary by a factor of 4 within the same type of housing (Pedersen et al., 1996) This may be caused by differences in the ventilation over the slurry between housings and by differences in waste management practices such as cleaning

Guenther et al (1995) were also confronted by a lack of measurement data in their global invemory of fluxes of volatile organic compounds (VOC) from vegetation Measurements represented only 26 of the 59 global land-cover types considered; the remaining land-cover types, including tropical seasonal forests and savannas, were assigned an emission on the basis of expert knowledge In this database, most of the simulated VOC emissions come from systems where very few or no measurements are available

- Functional types Guenther et al (1994) proposed emission factors for VOC for 91 woodland landscapes in the USA by combining emissions from 49 genera of plants In their global modelling ofVOC, Guenther et al (1995) used emission factors on the basis of the 59 land-cover types defined by Olson et al (1985) This aggregation causes considerable loss of information, as the detailed estimates for the USA vary by as much as a factor of 5 for various aggregated landscapes on the global scale

Yienger and Levy II (1995) used a combination of emission factors and modelling approaches to estimate global emissions of NO from soils They first calculated "biome factors" based on NO flux estimates from the literature These biome-dependent average fluxes were modified by an algorithm to account for pulse events of NO production following

wetting of dry soil and another algorithm to account for the effect of varying temperature Yienger and Levy (1995) also made an attempt to model the effects of NOx uptake by plants

on net NOx emission to the atmosphere They calculated absorption factors based on leaf area indices, and then multiplied these absorption factors by the estimated soil emissions to calculate net ecosystem emissions The model of Yienger and Levy has some mechanistic components, such as the wetting and temperature functions, but is primarily based on averaged biome factors that are not substantially different from an emission factor approach Davidson and Kingerlee (1997) also derived emission factors based on data for biomes from the literature Although in their study more soil NOx measurement data were used in comparison to Yienger and Levy's study (1995), the major differences between the two studies are the stratification scheme and t;L,~ coupling of environmental con.~ition descriptions

at the measurement sites with the functional types distinguished Davidson and Kingerlee (1997) presented a global annual emission which exceeds the estimate of Yienger and Levy (1995) by a factor of 2 It is clear that the differences between the two studies described will have an enormous impact on the results of atmospheric models

2.1.2 Regression approaches

Bouwman et al (1993) calculated the N20 emission from soils under natural vegetation using

a simple global model describing the spatial and temporal variability of the major controlling factors of N20 production in soils The basis for the model is the strong relationship between N20 fluxes and the amount of nitrogen (N) being cycled through the soil-plant-microbial biomass system The model calculates the monthly N20 production potential from five indices representing major regulators of N20 production (soil fertility, organic matter input, soil moisture status, temperature and soil oxygen status) These five indices were combined in the final N20 index (Figure 1) Comparison of the N20 index with reported measurements for about 30 locations in six ecosystems correlated with an r 2 o f - 0.6 (Figure l a) The resulting regression equation was used to calculate emissions on a l ~ 1 ~ resolution However, the correlation coefficient is not a robust statistical method (see Sofiev, 1999), and minor differences in only one of the measurement sites can cause major shifts in the correlation

coefficient (Bouwman et al., 1993) A major problem causing unreliability of the regression

equation is the lack of measurement data, particularly for a number of important ecosystems and world regions that have not been sampled at all It is not known how the model performs

in these areas (Figure 1 b)

- Uncertainties in global flux models Here, examples for oceanic flux models will be given, although very similar examples also exist for terrestrial models In aquatic systems, fluxes can generally not be directly determined Models commonly used describe fluxes on the basis of wind speed and anomalies of concentrations between surface water and air Nevison

et al (1995) calculated the air-sea exchange using three different relationships for the NzO-air

- Limitations of e c o s y s t e m m o d e l s Although models developed for specific ecosystems may show fewer uncertainties than global models, their value for extrapolation may be

10 A.F Bouwman, R.G Derwent and F.J Dentener

limited Mosier and Parton (1985) developed a model for the estimation of N20 fluxes over large areas of semi-arid grassland soils, accounting for spatial and interannual variability Model parameters were developed by relating N20 flux to soil moisture and temperature for two sites representing much of the variability in the Colorado shortgrass ecosystem Because

no time-series data of NO3 and NH4 + are available on the target scale of the study, the model was simplified with an empirical multiplier representing N availability It is especially empirical multipliers like these that cause problems when models are applied to other ecosystems with different environmental and climatic conditions

- Scale of process descriptions Some models seem to include an imbalance in the detail and the particular scale on which different processes are described For example, Li et al

(1992) developed a model to simulate N20 fluxes from decomposition and denitrification in soils on the field scale The model can also describe NO• fluxes by using soil, climate and data on management to drive three submodels (i.e thermal-hydraulic, denitrification and decomposition submodels) The management practices considered include tillage timing and intensity, fertilizer and manure application, irrigation (amount and timing), and crop type and rotation

One of the processes simulated by the model is microbial growth Since model results appear to be dominated by the effect of temperature and soil moisture, which operate at nearly all levels in the model, the question arising is whether there is an imbalance in the scales according to which processes are described The similarity of the results obtained for shortgrass ecosystems by Mosier and Parton (1985) with their simple approach to those of Li

Towards reliable global estimates of emissions of trace gases and aerosols 11

et al (1992) illustrates the need to match the scale of process description with that of the scale

at which the model is applied Comparisons of different models to predict N20 fluxes from fields (Frolking et al., 1997) reveal major differences in the simulated N gas fluxes from soils Apparently, the major problem in developing trace gas flux models is the description of soil processes that operate in "hot spots" in field models

which processes operate, a very practical problem is formed by the available model input data

To overcome this problem, sometimes summary models are developed on the basis of the detailed process model These summary models can be used to predict fluxes in regions with limited data availability Progress with the use of models on different scales for flooded rice paddy fields was made by Huang et al (1998) Understanding the processes of methane production, oxidation and emission in flooded rice fields enabled them to develop a semi- empirical model They also derived a simplified (summary) version of the model for application to a wider range of conditions but with limited data sets Huang et al (1998) hypothesized methanogenic substrates as being primarily derived from rice plants and added organic matter Rates of methane production in flooded rice soils are determined by the availability of methanogenic substrates a,~d the influence of environmemal factors Model validation against observations from single-rice growing seasons in Texas (USA) demonstrated that the seasonal variation of methane emission is regulated by rice growth and development A further validation of the model against measurements from irrigated rice paddy soils in various regions of the world, including Italy, China, Indonesia, Philippines and the United States, suggested that methane emission could be predicted from rice net productivity, cultivar character, soil texture and temperature, and organic matter amendments The detailed model and the summary model gave similar results (Figure 2), illustrating the advantage of using simplified models

2.1.4 F a r m nutrient balance m o d e l s

On the farm scale, trace gas fluxes occur in the stable, during grazing or during and after spreading of animal manure A model is therefore required to describe farm-scale processes and cycles For example, the model of Hutchings et al (1996) describes NH3 losses from animal housings, stored slurry, application of slurry and urine patches The model builds on knowledge acquired from various experiments and model studies of animal housing, waste storage and farming practices The model tracks the N input as animal feed until it is lost as NH3 The problem of applying farm-scale models is the variety in management styles occurring within groups of farms Representative farms or averages for a group of farms have

to be used to obtain aggregated data Differences in fluxes as a result of differences in management may disappear due to this aggregation

2.2 U n c e r t a i n t i e s in the s p a t i a l d i s t r i b u t i o n s

The environmental spatial data used as a basis for stratification schemes for delineation of functional types underpins the emission factor approach and, if sufficient attribute data are available, drives flux models When no spatial data are available to distribute activities or emissions, a proxy or surrogate distribution has to be used Clearly, this introduces an unknown uncertainty in the spatial distribution We will give a number of examples of databases that describe environmental conditions in aquatic and terrestrial ecosystems, emphasizing their uncertainties A comprehensive review of the data required for global terrestrial modelling can be found in Cramer and Fischer (1996) The list of examples given

12 A.F Bouwman, R.G Derwent and F.J Dentener

here is not intended to be complete, but does illustrate data limitations and aggregation problems The weaknesses and strong points in the databases discussed may serve to improve future database development The examples considered include databases for climate, oceans, soils and vegetation/land cover, as well as the problem of surrogate spatial distributions

2.2.1 Climate

The example of a database on current climate for a global terrestrial 0.5 ~ x 0.50 grid given by Leemans and Cramer (1991; update in preparation) includes average monthly, average minimum and maximum air temperature, precipitation and cloudiness values

- Data limitations The weather records were usually limited to at least five observational years from the period of 1931-1960 Not all stations considered have complete coverage Based on selection criteria, the final number of stations worldwide was found to be 6280 for temperature and 6090 for precipitation The cloudiness data set, defined as the number of recorded bright sunshine hours as a percentage of potential number, was based on fewer stations and often derived from estimated rather than recorded data

- Aggregation To aggregate the point data to a spatial grid an interpolation onto 0.5 ~ grid boxes was done using a triangulation network followed by smooth surface fitting For regions with no primary data, the temperature val.aes were corrected for altitude using an estimated moist adiabatic lapse rate and a global topography data set, while precipitation was not corrected; this was due to the more complex relationships between precipitation and altitude

- Uncertainty The major problem is the inappropriate data coverage for large areas of the world The uncertainty of temperatures is particularly high in mountainous areas because there are only a few weather stations in these regions and none of them are located on a clear altitudinal gradient The average moist adiabatical lapse rate for mountainous areas may result

in underestimation of temperatures for these areas The spatial precipitation patterns resulting from straight interpolation of measured values causes great uncertainty in areas with sparse data coverage Although the major annual cloud dynamics are represented, the regional reliability of the cloudiness data is low

2.2.2 Oceans

The best known chemo-physical global ocean data sets are included in the World Ocean Atlas

(Conkright et al., 1994; Levitus and Boyer, 1994a, b; Levitus et al., 1994) This database

includes spatial information on a l~ 1 ~ grid at various depths between 0 and 5500 m below the surface for ocean temperature, salinity, dissolved oxygen, apparent oxygen utilization, oxygen saturation, phosphate, and nitrate and silicate Data for temperature and salinity have a monthly time resolution and apply to depths between 0 and 1000 m below the surface; those for dissolved oxygen, apparent oxygen utilization and oxygen saturation are on a seasonal temporal scale and phosphate; nitrate and silicate concentrations taken on an annual basis

- Data limitations The World Ocean Atlas is based on many observations For example, the temperature data set is based on 4.5 million profiles Although the number of observations

is much higher than that used to produce the soil, vegetation/land cover and climate databases, there is a problem of areas with a low density or absence of observations; furthermore, the timing of the measurements may differ between profiles

- Aggregation The data at the observed depth were interpolated to standard depths The accuracy of the observed and standard level data was checked and flagged using a number of procedures The point data for depth profiles were interpolated onto a 1 o grid

- U n c e r t a i n t y There are many regions where measurements are scant or even absent To describe the density of observations, there are accompanying mask files for all the data listed

Towards reliable global estimates of emissions of trace gases and aerosols 13

above, containing the number of grid points with data within the radius of influence surrounding each grid box If a grid box contains three or fewer observations within its radius

of influence, the mask value for that 1 ~ grid box will be zero This file is used in plotting routines to "mask" or cover up areas with three or fewer observations

2.2.3 Soils

Soil fertility, and soil chemical and physical parameters, play an important role in the production and exchange of trace gases Recently, a 0.5 ~ • 0.5 ~ global soil database was developed on the basis of an edited version of the 1:5 million scale FAO Soil Map of the World (FAO, 1991), combining geographic information on soil types with a set of representative soil profiles held in a profile-attribute database (Batjes and Bridges, 1994)

- Data limitations The density of available soil profile data varies from one region to the other Important geographic gaps are in China, the New Independent States and the Northwest Territories of Canada Similarly, a number of soil units are underrepresented in the profile database; these units account for about 28% of the terrestrial globe of which total Lithosols (shallow soils) account for about 40%

- Aggregation The FAO Soil Map of the World is a compilation of many national and regional soil maps Therefore coverage is not spatially constant The soil profile information for each soil unit was coupled to the soil units distinguished region-wise Based on the number of profiles available, statistical analysis was performed by Batjes (1997), allowing refinement of ratings for soil quality in global environmental studies

- Uncertainty The variability of the reliability of the spatial information has already been mentioned The attribute files containing soil profile data in Batjes and Bridges (1994) represent a major improvement on the FAO soil map as such However, this aggregation may not realistically describe the variability actually occurring within a soil unit in regions where the density of observations is low

2.2.4 Vegetation~Land cover

Similar to the soil information, land-use and land-cover information is required to scale up information from the field to landscapes or ecosystems Two examples of widely used vegetation/land-cover maps are those compiled by Matthews (1983) and Olson et al (1985) with 1 ~ and 0.5 ~ spatial resolution, respectively A recent development is the creation of a global 1-km resolution global land-cover characterization (Loveland et al., 1997) based on remotely sensed data For the pan-European region (from Gibraltar to the Ural and from the North Cape to Athens) a land-cover database with a 10% 10 minutes resolution was developed (Veldkamp et al., 1996)

- Data limitations Matthews (1983) used the Unesco (1973) vegetation classification scheme, while the database by Olson et al (1985) is based on a land systems grouping Estimates of the extent of vegetation/land-cover types excluding cultivated land show a considerable difference between the two databases The global area of cultivated land is similar in all the maps and corresponds well with FAO statistics, although regional discrepancies may exist The Olson and Loveland et al databases include estimates for carbon stocks in each land-cover type Apart from definitional problems, there is generally a major lack

of observational data describing the properties of the vegetation/land-cover types distinguished

As in the soil database of Batjes and Bridges (1994), the map unit characteristics will be included in attribute files, allowing use of the data for different purposes in a variety of models

Aggregation The Matthews and Olson databases were compiled from maps, atlases and

14 A.F Bouwman, R.G Derwent and F.J Dentener

other information available For spatial aggregation satellite observations may form a considerable improvement The 10 ~ x 10 ~ resolution for the pan-European region (Veldkamp

et al., 1996) includes eight classes produced from a combination of spatial data in vector format (based on various sources, including satellite data) and tabular statistical data A calibration routine was used to ensure that no land-use class deviated more than 5% from the statistical information The Loveland et al database is derived from 1-km Advanced Very High Resolution Radiometer (AVHRR) d:,,a, spanning a 12-month period (April 1992-March 1993) It is based on seasonal land-cover region concepts, which provide a framework for presenting the temporal and spatial patterns of vegetation in the database

- Uncertainty Major uncertainties in the traditional databases, such as Matthews (1983) and Olson et al (1985), are seen in the classification scheme used, the underlying data and the aggregation method, which is illustrated by the disagreement in the spatial distributions between these two databases The database of Veldkamp et al (1996) may suffer from the small number of types distinguished; this may not allow a proper description of the observed variability necessary for ecosystem and trace gas studies However, the combination with soil and climate data may form an improvement here The database also lacks data on the characteristics of the vegetation type itself in the form of attribute data Since the Loveland et

al database is still in development, its uncertainty is as yet unknown A review of the use of remote sensing and other data in vegetation mapping is given by Estes and Loveland (1999)

2.2.5 Surrogate distributions

When the exact location or distribution of an activity or process is not known, surrogate distributions are used to distribute activities, volumes or emissions over the grids For example, the grassland distribution is generally used to distribute cattle populations, while for other animal categories the rural human population distribution or the distribution of arable land is used as a surrogate distribution However, the human population distribution is generally not well known in rural areas, as statistics and atlases give data on populations in major towns only Using surrogate distributions may be realistic in some regions However, in others with specific stratifications of management, environmental or demographic conditions, surrogate distributions may cause major errors (see, for example, the dairy cattle discussed in 2.4)

2.2 6 General remarks

The major uncertainties in databases are generally related to the scarcity of data, and variable density of data coverage and quality With reference to the data problem, the mask files (containing the number of grid points for data within the radius of influence surrounding each grid box) provided in the ocean database form a good tool for describing the data density and the point-by-point accuracy or reliability in other databases as well

Compared to the classification schemes for vegetation and land cover in the traditional maps and databases, satellite observations may provide a more flexible way of describing ecosystem characteristics Attribute files with descriptive data of the map units distinguished (e.g in the soil database of Batjes and Bridges, 1994) are very useful for modellers These attribute data also enable performance of statistical analysis of the data by unit Furthermore, correction of the satellite data with actual statistical information is a good way to improve the accuracy of the spatial data Finally, a combination of vegetation/land-cover data with climate and soil information may provide a basis for classification into functions

Towards reliable global estimates of emissions of trace gases and aerosols | 5

2.3 Uncertainties in the economic data on land use

The major forms of economic land use activities generating emissions of trace gases include livestock production, crop production and forestry Livestock production is the most complex system In livestock production systems, trace gas fluxes can be determined in a stable fi~r either individual animals or a group The comp.ete production system, from feeu to excretion and emission in the stable and during grazing, has to be known for extrapolation of these measurements For example, to estimate NH3 emissions from animal manure during storage and during and after application as a fertilizer, we need to know the number of animals in each animal category (e.g dairy cattle) according to age class, live weight; N content and relative share of the various amino acids, N use efficiency (feed conversion to milk and meat); housing system and period of confinement, and form, mode and period of storage of manure Further, we need to know weather conditions during spreading (turbulence, air temperature, air humidity and rainfall), properties of the soil to which the manure is applied, amount of manure per unit area, mode of manure application and the period between application and cultivation

Outside Europe and North America all these data are scant Data on animal populations by category, and within a category (according to age and weight class) are almost non-existent For many countries only the total number of animals within a category is available for a specific year Data are not available on some animal categories, such as house pets, horses, buffalo, donkeys, camels, or on housing, and the type and form of manure Estimates for regions within countries may be availai~:e, but do not always correspo,d to the official statistics or are outdated Data on the coverage of stored manure, which may highly vary in effectiveness, are lacking Geographic data on the application rate and timing of manure application, soil conditions, and weather conditions during application are not available In addition to spatial variability, manure application rates, and mode and timing of application, show a strong interannual variability, which is not easy to include in scaling exercises Storage and spreading of manure are regulated by law to reduce emissions in a number of countries It

is difficult to obtain information on the actual observance of these laws and the emission reductions achieved

Data on crop production systems that are essential for estimating trace gas fluxes envelop fertilizer use (including animal manure) by type and by crop, timing and mode of fertilizer application, amount and timing of field-residue burning, animal waste management, number

of rice crops per year combined with soil and water management practices and fertilizer application rates Such data may be available for regions within countries but may not always correspond to the official statistics or may be outdated

Global forestry data are available from FAO statistics and assessments ~z.g FAO, 1995) However, information on the species planted and forest management are difficult to obtain In assessments of trace gas fluxes it is generally important to know the amount of above- and below ground carbon in a certain forested area Global data on carbon in vegetation can be obtained from Olson et al (1985), for example, and carbon in soils from such sources as Batjes (1996)

In summary, the economic and attribute data generally have to be inferred from aggregated country totals for the three land-use systems Where the geographic distributions within countries are not directly available, data have to be distributed over a spatial grid or subnational regions In this case surrogate distributions will have to be used (see section 2.2)

2.4 Uncertainties in the temporal distribution

Temporal patterns of trace gas fluxes vary in space This poses difficulties for integration of

16 A.F Bouwman, R.G Derwent and F.J Dentener

fluxes over spatial units Spatial aggregation causes considerable loss of information on temporal flux patterns However, the paucity of measurement data often makes generalizations unavoidable Generalization is usually done by treating a landscape as a composite of representative soils or farms with average waste characteristics, management and weather conditions, or by treating populations as a group of identical members Such generalizations may lead to errors in temporal distributions due to averaging procedures The temporal pattern of estimates derived for a group of average farms may differ from the sum of all individual farms Generally, different grazing systems co-exist within regions For example, in dairy production systems part of the production takes place in stables only The animal waste collected in the stables is at~plied to grassland or croplands at different times Hence, the temporal pattern of gas fluxes is determined by the grazing systems occurring in the landscape considered

Errors caused by aggregation of groups of farms may be particularly large for N gas species This was shown by Schimel et al (1986), who analyzed the cycling and volatile loss

of N derived from cattle urine at lowland and upland sites in a shortgrass steppe in Colorado, USA The NH3 losses were measured in microplots representing three soil types typical for the shortgrass steppe landscape Seasonal rates of urine and faeces deposition were mapped by landscape position, allowing for simulation of responses of animals to microclimate and forage availability, and differential use of upland and lowland pastures This provided variation in the proportion of total excretion vulnerable to loss Urine deposition was higher during the growing season when forage-N levels were high, and highest in lowland soils Simple aggregation of the spatial patterns of deposition and loss would have resulted in a calculated loss of NH3 of a factor of 7 higher than for sophisticated stratification on the basis

of the observed seasonal and spatial variability Studies of gaseous fluxes are vulnerable to this type of error because fluxes can be intermittent and patchily distributed in space

Methane fluxes from rice fields are also extremely variable in time and space Measurements for individual fields indicate diurnal and seasonal patterns caused by rice growth and development (e.g Huang et al., 1998), which can best be described using process models (see above) Additional pulses caused by management practices are more difficult to describe in flux models or emission factor approaches because the statistical information on management is sparse and often absent, as discussed above An attempt to distinguish seasonal variability in rice global cropping patterns was made by Matthews et al (1991), who presented cropping calendars for rice production worldwide This stratification serves as a basis for applying flux models with the corresponding data on soil, water and crop management

In summary, there is a problem in scaling-up of loss of information on temporal variability due to spatial aggregation or generalization This problem may occur on any scale Sophisticated and carefully chosen stratification schemes for the delineation of functional types within landscapes may help in reducing the aggregation loss of information on temporal variability Temporal patterns can best be described by using process models

3 Spatial and temporal resolution of current emission inventories and CTMs 3.1 Emission inventories

In the previous sections we discussed a number of major problems that occur during the pro- cess of scaling-up data using different approaches on different scales In this section we will present a number of global and regional inventories for selected trace gas species and sources

of emissions which have been developed for scientific purposes We will not discuss these

Table 3 Global inventories of emissions of trace gases and aerosols from aquatic and terrestrial ecosystems for a number

of gas species with a spatial resolution of 1 o • 1 o longitude-latitude representative for the period around 1990

Category C O 2 CH4 CO VOC N20 NO• NH3 S/SO• Aerosols Black

carbon

Land-use related sources

Crops, fertilized fields

Animals (including enteric

fermentation, animal

waste)

Biomass burning (including

waste and fuelwood com-

(1997); 5, Lerner et al (1988); 6, Fung et al (1991); 7, Olivier et al (1996); 8, Spiro et al (1992); 9, Matthews and Fung (1987); 10, Guenther et al (1995); 11, Nevison et al (1995); 12, Lee et al (1997); 13, Benkovitz and Mubaraki (1996); 14, Tegen and Fung (1995)

a Inventory based on estimates of burnt dry matter burnt can also be used for other gases

b Inventory could be based on Bouwman et al (1997)

c Inventory is in fact based on emission factors for biomes coupled with a mechanistic model to produce temporal patterns

of fluxes

d Soil dust emissions and transport are simulated on the basis of GCM-based wind fields

inventories on the country or provincial (subnational) scale being prepared for non-scientific purposes (e.g national communications in the United Nations Framework Convention on Cli- mate Change) The inventories listed in Tables 3 and 4 represent data for the early 1990s or late 1980s These lists are not intended to be complete but merely to illustrate the current

"state-of-the-art" emission inventories We have not presented earlier work, assuming that the methodology of early inventories is incorporated into the more recent ones Some of the global inventories were based on regional data or inventories, and their spatial and temporal resolutions are not lower than those in the regional inventories

The reported spatial resolution for most regional and global inventories is 1 ~ 1 o (Table 3) However, in many cases the real spatial resolution is much lower For example, when inventories are based on the emission factor approach for vegetation types or biomes, the spatial detail is the biome and not the grid size Emission factor approaches were used in many inventories, including all those for CH4, VOC, NO• and NH3 As discussed above, some

of these inventories use simple rules or models to distribute fluxes over time

The most common temporal resolution of the inventories is one year Some inventories have a monthly distribution; the inventory of NO• fluxes from soils has a temporal resolution

of one day This database was compiled by using the emission factor approach combined with

Table 4 Regional and continental inventories that include land-use related and biogenic emissions of a number of gas species with different spatial and temporal resolutions

Region Species/sources Spatial scale Temporal scale Reference North America CO, CH4, VOC, NOx NH3, SO2, 80 • 80 km h 1

HCI for all known sources Europe SO2, NOx, NH3, NMVOC, CH4, Nuts regions, converted ya 2

CO, N20, CO2 for all known to 50• km grids + sources point sources Europe, Russian SO2, NOx, NH3, NMVOC, C H 4 , 50• km grids + point ya 3 Federation, United CO, NzO , CO 2 for all known sources

States of America sources

Europe SO2, NO• NH3, VOC CO for all 2~215 ~ grids (Ion • lat.) Ya 4

known sources The temporal resolution is indicated by y (year), or h (hour)

References: 1, EPA (1993); 2, EEA (1997); 3 UN (1995); 4 Veldt et al (1991)

a with time profiles for conversion to monthly or shorter time periods

a simple model based on temperature and precipitation data from one particular GCM Some regional inventories include rules for distributing emissions in time, for example, on a daily or hourly basis (Table 4)

National inventories will be produced in the framework in the IPCC Methodology for National Inventories Most of these inventories will be compiled on the basis of default annual emission rates, as measurement data are not available in most countries This temporal resolution of one year is similar to that of most of the global inventories

3.2 Atmospheric models

It is difficult to be definite about the current state of the art in CTMs since they continue to be developed as scientific understanding grows and as computers increase in soeed and capacity Meteorological data with a time resolution of 1-6 h are typical of data used, while the spatial resolution in the models is typically a few degrees latitude and longitude Models have typical runs of a few seasonal cycles: this is considered a mere snapshot when used for climate calculations Model processes are usually handled with the same spatial and temporal resolution as the meteorological processes

It is important for two main reasons to accurately assess the trace gas fluxes between terrestrial and aquatic ecosystems and the atmosphere in CTMs Firstly, CTMs need to describe these trace gas fluxes realistically so as to accurately assess the trace gas life cycle on the global or regional scale Secondly, the CTMs may need to give an accurate representation

of the trace gas flux for a particular ecosystem or region In the first case, the spatial distribution of the flux may not be so crucial but it is important to achieve the correct total burden In the second case, the flux to particular sensitive ecosystems may be a more important variable in the model than the total global flux

In considering model estimates of trace gas fluxes to terrestrial and aquatic ecosystems and their unce~ainties, there are a number of issues to consider The CTM needs to describe the transport of the trace gas to the ecosystem and to present the trace gas to the ecosystem at the correct concentration level and on the correct time scale Clearly, the greater the distance travelled from the point of emission and the smaller the area of the ecosystem, the greater the associated uncertainty

For regional-scale transport close to the planetary boundary layer, current CTMs should produce concentrations that are within the range o f - a factor of 4 or more for primary

pollutants as monthly or seasonal averages in flat terrain 10-100 km downwind of sources (Jones, 1986) However, trace gas fluxes may often involve some form of chemical processing

in the atmosphere downwind of the point of emission, which may contribute considerable additional uncertainty in modelled trace gas fluxes

Figure 3 illustrates some of the issues on validation of current generation CTMs against observational data for the short-lived trace gas, sulphur dioxide (SO2) The figure shows the annual average model SO2 concentration for the 5 ~ • 5 ~ grid square covering much of England along with the monthly mean observations for 19 monitoring stations On this scale, there is significant spatial variability between the individual measurement sites, which in itself covers

a range of up to a factor of 8 Such a range is likely to be significantly larger than the uncertainty in emissions Furthermore, variability is significant at a finer time resolution e.g daily or hourly

The uncertainty in coarse-resolution CTMs operating at 5 ~ x 5 ~ which approximates the state of the art CTMs, is likely close to a factor of 4 up and down for short-lived trace gases with significant ecosystem sources and sinks and existing in a complex terrain

The representation of the trace gas exchange processes in the CTMs at the ecosystem scale will introduce further uncertainties, the magnitudes of which are crucially dependent on the nature of the exchange process involved Dry deposition processes are thought to be the simplest processes representing the concept of a dry deposition velocity In this way, many of the problems of scaling trace gas fluxes can be side-stepped with a simple parameterization Clearly, there is a huge gap in scale between the available dry deposition studies on the leaf or canopy scale and the coarse grid squares of the CTM

Wet deposition is a sporadic process which is difficult to describe adequately in models The coarse spatial resolution of the models is certainly an issue but perhaps more important is their neglect of the detailed microphysical and chemical processes thought to be occurring in rain clouds Simulated global- or regional-scale wet deposition fluxes are available with reaso-

Figure 3 Simulated concentration of SO2 using the STOCHEM model for the 5 x 5 0 grid square covering most

of the U.K and monthly mean observations for 19 monitoring stations Source: Stevenson (1998)

20 A.F Bouwman, R.G Derwent and F.J Dentener

nable accuracy, but this accuracy deteriorates as spatial scales decrease to the catchment or landscape scales Topography is a crucial factor in driving the orographic enhancement of wet deposition In coarse-resolution CTMs the topography of all but the highest mountain ranges

is necessarily averaged out, thus removing a major influence on model wet deposition fluxes

to sensitive catchments There is a consequential reduction in model estimates of cloud water deposition as the topography is smoothed out by model spatial resolution

Trace gas exchange with terrestrial and aquatic ecosystems is not always a one-way process, as emission and resuspension may occur simultaneously (see Conrad and Dentener, 1999) Ammonia emissions are difficult to represent accurately in models because they are sporadic and depend on local factors, which are highly variable Soil moisture and animal husbandry are two such factors which are difficult to be specific about, but which have a

significant influence on ammonia emissions (Bouwman et al., 1997) Resuspension of sea-

salts and wind-blown dust is often driven by high winds, which can be adequately represented

in CTMs However, the state of the terrestrial surfaces, whether wet or recently ploughed, may have a pronounced influence on resuspension, and these local factors are not often well- defined on the coarse scales used in the CTMs

3.3 Comparison of CTMs with emission inventories

With the exception of the spatial resoh".:.on of the emission inventories which meet the requirements of current CTMs, there are major inconsistencies to remain between the CTMs and the emission inventories which drive them The most striking discrepancy between CTMs and inventories is in the temporal scale, which is generally one year for the inventories and 1-

6 hours for the CTMs Most CTMs include routines based on hypotheses on temporal flux distributions at the model scale, or assumptions on temporal patterns are provided with the emission inventories (see Table 4) Another way is to incorporate the trace gas flux model in the atmospheric model, as done for example in some CTMs for NOx from soils

For reactive species with short atmospheric lifetimes such as NH3, NOx and VOC, the temporal scale gap is a more serious problem than for long-lived species An additional gap between inventories and CTMs is the number of VOC species; here, some of the mechanisms describing the chemistry in CTMs require a much larger number of species than included in current inventories

A general major problem is that it is not always possible to ensure that consistent land use and meteorological data are used throughout the modelling system including the emission inventorie~ Furthermore, there are scaling problems with all aspects of CTI~ input data, some

of which are caused by limited computer resources, others by the focus of the modelling system and yet others by lack of current understanding

Turning to validation of emission inventories, the emission fields for long-lived trace gases can be tested using CTMs on the basis of concentrations, trends, and seasonality and spatial gradients of concentrations, as the chemistry is less crucial for long-lived species with fewer fluctuations over the year For other species, deposition rates can be used to validate model results A discussion of validation tools is, however, outside the scope of this paper We refer

to Heimann and Kaminski (1999) for a review of inverse modelling and atmospheric monitoring networks, Trumbore (1999) for a review of the use of isotopes and tracers in validation and scaling of trace gas fluxes, and to Sofiev (1999) for a discussion on validation and representativeness of measurement data A review of the use of remote sensing techniques

to determine atmospheric concentrations is given by Burrows (1999)

Towards reliable global estimates of emissions of trace gases and aerosols 21

It should be possible to produce estimates for most species and sources with a greater temporal resolution However, the key problem involved in increasing the temporal resolution

is the sparsity of data for use as a basis for flux estimates and a lack or even absence of independent data to validate fluxes Available data may be appropriate to validate the temporal variability or the functional relationships between environmental conditions and fluxes In general, it becomes increasingly difficult to find tools for validation as the level of detail of the temporal scale increases In some cases such data are inadequate or even absent (e.g deposition fluxes, concentrations of short-lived species)

The spatial resolution of inventories in our review suggests the level of detail as being adequate for current CTMs However, the real spatial resolution of most inventories is much lower than suggested by the 1 ~ reported This is caused by the use of emission factors for biomes and functional types, and by the uncertainty and resolution of the environmental spatial data used

The major recommendations following from the examples discussed can be summarized as follows:

- E m i s s i o n factor approaches Where emissions are described with emission factor or regression approaches, variability can be used instead of the usual practice of averaging out the heterogeneity This is done, for example, by presenting frequency distributions for regions

or functional types, or the standard deviation for grid boxes, In many cases the point-by-point uncertainty is not known However, even the indication of the maximum and minimum values could be more helpful than the mean alone for sensitivity and quantitative uncertainty analysis

- F l u x m o d e l s Flux models should be used where possible to replace traditional emission factor approaches Firstly, models, which are descriptions of current process knowledge, are preferred above simple rules such as those applied in CTMs to produce temporal distributions Secondly, intemal consistency of CTMs is improved by incorporating the flux models

In trace-gas flux models there is often an imbalance between the level of detail by which different processes are described The relationship between scale, the model approach and the model parameters selected is very important in this respect On a higher scale the data availability, generally poses a problem when using detailed process models In this case, simplified or summary models are expected to interpret field experiments with limited information The aim of simplifications is to make the model applicable to a wider area with limited data sets Developing such ranges of models from the micro-scale to field scale and summary models to be used for extrapolation to other sites with different conditions is extremely useful Summary models will also help to develop a better understanding of how to select the key variables to be used for specific scales

- E n v i r o n m e n t a l d a t a The spatial data on climate, oceans, soils, land cover and land use which are commonly used as a basis for scaling of trace fluxes have four general characteristics: (i) their uncertainty is regionally variable but generally unknown in the spatial distributions; (ii) data classifications are always aggregations (iii) classifications used are generally not easily translated into other classifications; (iv) classifications cannot be easily translated into properties or regulating factors of trace gas fluxes In view of these

22 A.F Bouwman, R G Derwent and F.J Dentener

characteristics the use of common databases should be promoted

Geographic databases coupled with attribute files for the various map units distinguished is one way to at least describe the heterogeneity of the properties within a class Examples of this approach are the soil database and the land-cover characterization discussed in this paper Combining vegetation/land-cover data with climate and soil information may provide a basis for classification according to function Finally, there is a need for compensation and recognition of so-called data collectors to encourage continued critical data collection, harmonization and analysis

- F u n c t i o n a l types Where distinct and easily identified differences in structure and composition of aquatic and terrestrial ecosystems coincide with the functions or management conditions relevant to trace-gas fluxes at the scale considered, the delineation of functionally different types or production/management systems provides a useful basis both for measurement strategies and scaling Appropriate selection of classes may lead to reducing the number of sites to be sampled so as to derive a reliable flux estimate Maps provide a useful basis for delineation, and in recent years remote sensing of ecosystem characteristics has been used increasingly for classification and modelling (see Estes and Loveland, 1999) Such approaches use the variability of a system or landscape instead of ignoring it, sometimes with unexpected consequences It is very important to select appropriate stratification schemes for functional types, both for the scale of the exercise and the available spatial data

- Aggregation Aggregation always leads to a loss of information The variability in space is reduced and the uncertainty in the temporal patterns is increased by spatial aggregations The problem of errors in temporal distributions as a result of spatial aggregation can be reduced by delineating functional types within a system Scaling based on delineations with finer spatial data may be different from that derived from data with lower resolution In general, it is better to aggregate model results than to aggregate the spatial data before modelling Aggregation in the form of delineation of functional types as a basis for scaling generally decreases the uncertainty, and allows one to determine the uncertainties as discussed above

- I n t e r a n n u a i variability Some processes in terrestrial and aquatic ecosystems show con- siderable year-to-year variation Hence, in such systems with large interannual variability, in- ventories representing the long-range average have less value than time series of flux estimates

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A.F Bouwman, editor

1999 Elsevier Science B.V

METHODS FOR STABLE GAS FLUX DETERMINATION IN AQUATIC

AND TERRESTRIAL SYSTEMS

R.L: Lapitan l, R Wanninkhot e and A.R Mosier 3

1 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523 USA

2 National Oceanographic and Atmospheric Administration, Miami, FL 33149 USA

3 Correspondence to: U.S Department of Agriculture, Agricultural Research Service, P.O Box E, Ft Collins,

CO 80522 USA

I I n t r o d u c t i o n

Despite the world's keen awareness of the potential global warming effects of greenhouse gases, atmospheric loading of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N20) from anthropogenic sources, and aquatic and terrestrial components of the biosphere continues

at very high rates Estimates made in 1994 showed increases of 1.6 ppmv yr -1 8.0 ppbv yr -1 and 0.8 ppbv yr -1 for CO2, CH4 and N20, respectively (Houghton et al., 1995) The present

atmospheric concentrations of these gases and rates of increase could lead to irreversible climate change To increase our confidence in projections of trace gas increases we must improve mass balance accounting of sources and sinks of these gases, on a large scale (> 1 km) The uncertainties in the estimates can be attributed to the wide spatial and high temporal heterogeneity at the surface (e.g soil, vegetation, water) - atmosphere interface, inadequate accounting, and assessments of the source-sink strengths of these gases (Bouwman, 1990) The problems contributing to the latter factor are the unavailability of sensitive analytical devices for field measurements of trace gas fluxes, lack of effective sampling design for reducing variabilities in point measurements, and lack of proven mechanistic tools for reconciling flux measurements taken at different spatial and temporal scales Additionally, it should be pointed out that existing models, used for extrapolating small-scale fluxes to regional and/or global scales, contained intrinsic uncertainties in their assumptions, para- meterization, and analytical/numerical representations of the control processes that can further magnify the uncertainties of estimates

The primary considerations in the choice of method for measuring gas fluxes include the objectives of the study, type of ecosystem under study, cost, infrastructure, and logistical support, gas species in question, and availability of precise analytical instruments having appropriate response rates for measuring gas fluxes Fluxes of trace gases from terrestrial systems have been measured by enclosure or micrometeorological techniques Because of the enclosure and limited spatial resolution of the closed-chamber method it was found suitable for detecting small fluxes of trace gases (e.g N20), studying processes, and identifying sources of spatial variations controlling gas fluxes (Hutchinson and Mosier, 1981; Mosier, 1989; Livingston and Hutchinson, 1995) Micrometeorological methods provide non- destructive, integrated measurements of gas fluxes over large areas, but generally require large, uniform fetch Tower-based and airborne eddy flux correlation methods require expensive fast-response sensors and logistical support Depending on the gas species in

30 R.L Lapitan, R Wanninkhof and A.R Mosier

question, analytical instruments must be sensitively accurate to detect one-tenth of the mean concentration difference between updrafts and downdrafts for typical CO2 (-2x 10 6 kg m 2 s-l), CH4 (1 x 10 -9 kg m -2 s-l), and N20 (4x 10 -l~ kg m -2 s -1) fluxes (Denmead, 1979; Desjardins et al., 1993) It should be pointed out that the measured range of fluxes of CO2, CH4 and N20 gases vary among terrestrial ecosystems (Table 1) Thus, depending on the vegetation and physiographic characteristics of the system, accompanying analytical methods must be able to detect the wide range of fluxes of the gas species desired

The field methodologies for measuring trace gas fluxes in terrestrial systems have been developed and have changed little for the past 15 years The strategies of field sampling and computational procedures for deriving trace gas fluxes using these methodologies have been well documented; such as, for chambers (Mosier, 1989; Hutchinson and Livingston, 1993; Livingston and Hutchinson, 1995), micrometeorological (Fowler and Duyzer, 1989; Denmead and Raupach, 1993; Desjardins et al., 1993), and aircraft-based (Desjardins and MacPherson, 1989; Desjardins, 1992; Desjardins et al., 1993; Choularton et al., 1995) More recent advances made in surface and atmospheric trace gas flux measurements were seen with the employment of fast-response high resolution spectrometers, such as the tunable diode laser differential absorption (TDL) and Fourier-transform infrared (FTIR) spectroscopy Specifically, these sensitive spectrometers extend the capability of existing methods to detecting small in situ episodic fluxes and gradients of CH4 and N20 trace gases (Kolb et al.,

1995) Hence, in this paper discussions of the methods of measuring gas fluxes in terrestrial systems are limited to providing a brief overview of the methods with examples taken from more recent studies employing advanced spectrometers Descriptions of the individual methods are focussed on their merits and limitations as these would determine the suitability

of the method's application to a system, given the extent of the spatial heterogeneity observed The exchange of nonreactive trace gases between water or soil and the atmosphere are regulated by the same chemical, biological, and physical parameters However some methods

of measurement differ because aqueous systems are, in general, relatively more homogeneous than terrestrial systems and fluxes of scalar entities are often smaller Mass balance approaches in the water column are often used to quantify air-water fluxes because micrometeorological techniques are difficult to employ Refinements of the latter metho- dologies are continually being developed particularly in accurately estimating the gas transfer velocity A fundamental treatment of the principles and governing equation of air-water gas exchange is helpful as a priori reference for discussions on how and wl~ere the different methods provide improvement in gas flux The application of these methods to measuring gas fluxes, including problems and uncertainties, in aquatic systems is described in section 7

2 Scales of spatial and temporal heterogeneities in gas flux measurements

The scale of spatial heterogeneity in the landscape limits the extent of applicability of a method and validity of its assumptions for gas flux measurements Since flux measurements

of gases are one-dimensional, a priori knowledge of the extent of horizontal variabilities of the dynamic factors affecting surface-air gas exchange is important for accurate identification

of sources of gas entities Breaks in horizontal continuities of surface properties, spatial pattem of climate variabilities, and magnitude of gas exchanges between the surface and the atmosphere define the boundaries of natural systems Types of land cover, mountain barriers, and sea-land transition account for the largest horizontal scale of variability in gas fluxes, extending to the order of > 1 km It should be appreciated that gas flux measurements at this

Methods for stable gas flux determination in aquatic and terrestrial systems 31

scale are taken as the mean integrated response from the more commonly persistent features

of the landscape

Atmospheric transport of gases encomp_' ssing areas of this space scale follows large-scale atmospheric mixing phenomena, such as the vertical transport due to convective boundary layer flow, cloud mass flux, and synoptic-scale transport by trade winds and storm systems (Raupach and Finnigan, 1995) Around the transition edge between the land and marine environment, transport of gases to the atmospheric boundary layer is coupled with the heat and water exchanges associated with the cyclic land breeze - sea breeze system (Merrill,

1985) Qualitative in situ identification of gas sources has been made using hydrodynamic and

radioactive tracers (Reiter, 1972;1978), but quantitatively, the temporal variations in gas concentrations and effects of atmospheric mixing (e.g., inversion) on the budget of gases in the atmospheric boundary layer can only be resolved using numerical models (Raupach, 1991;

Raupach et al., 1992; Denmead et al.,1996)

Within a region or at the ecosystem level, the composition and properties of the surface and surface cover align with the climate (i.e., temperature and precipitation) variabilities and land management systems Horizontal gradient of scalar entities (e.g., heat, water vapor, and gas) can be of the order of 102 to 103 m depending on the persistence of uniform surface terrain, type of vegetation, and surface cover Transport of gas entities downstream from the source is

determined by the rate of turbulent mixing as wind blows steadily over the surface In situ

identification of the source and trajectory monitoring of gas fluxes can be accomplished using non-dispersive release gas (e.g., SF6) in conjunction with micrometeorological techniques and sensitive analytical devices (IAEA, 1992)

The smallest scale of variability in gas flux measurements is of the order of < 1 m The variations in gas flux at this space scale can be attributed to patchiness and type of ground cover, plant species composition, and differences in soil properties driving the biogeochemical processes effecting soil-air gas exchange Transport of gas from the source can be laminar or turbulent depending on the prevailing wind field In an unobstructed vegetative system, the sources and sinks of the gas can be inferred from the variations in gas concentration profile and vertical wind velocity within the canopy, such as following the inverse Lagrangian dispersion model developed by Raupach (1989) At this space scale, the use of enclosures permits isolation of the source of gas fluxes and eliminates the uncertainties associated with numerical calculations and estimates of the turbulent eddy transfer coefficients required by micrometeorological techniques

For reconciling flux measurements at various scales of heterogeneity, a flux footprint which describes the contributions, per unit emission, of each element in the area observed by

the sensor located at a fixed height above the surface has been suggested (Shuepp et al., 1990;

Horst and Weil, 1992) The flux footprint is determined by the surface properties (e.g roughness length, vegetation height), wind speed, and atmospheric stability Flux footprint can

be obtained analytically (Schuepp et al., 1990) or numerical modelling approach (Leclerc and

Thurtell, 1990; Horst and Weil, 1992); either approaches provide closely similar footprint

estimates (Hargreaves et al., 1996) The estimated flux footprints for the different methods of

measuring gas fluxes are given in Table 2 By weighting the area-integrated flux with the flux footprint, the sources of spatial variations in gas flux measurements from micrometeorological

measurements can be identified (Hargreaves et al., 1996), potential errors and differences between micrometeorological methods can be determined (Wienhold et al., 1995), and

comparative analysis between chambers and integrative methods of measuring gas fluxes can

be made (Christensen et al., 1996)

The temporal fluctuations in ambient atmospheric conditions can be as significant a source

of uncertainties as the surface horizontal heterogeneities in gas flux measurements Sudden,

Superscripted indicate references c

5.6 (5) 0.4_17.1 (11) 0.4_5.6 (1~)

- 10- -3 8 (19'20'21"22) 6.2 (12)

-0.004- - 0 0 1 6 (34)

aA, closed chamber, B, open chamber, C, micrometeorology, D, aircraft-based sensors and E, convective boundary layer budget Positive values of annual input and fluxes indicate

, ,

r~

u~take and negative values indicate emissions of the gas

v, vegetative stage, s, senescent stage

el, Denmead et al (1979); 2, Galle et al (1993); 3, Ritter et al (1992); 4, Denmead (1993); 5, Seiler et al (1984); 6, Cicerone and Oremland (1988); 7, Schtitz et al (1989); 8, Sass et

al (1990); 9, Lauren and Duxbury (1993); 10, Keller et al (1986); 11, Schtitz et al (1990); 12, Aselman and Crutzen (1989); 13, Harris et al (1982); 14, Houghton et al (1983); 15, Lauenroth and Milchunas (1992); 17, Bronson and Mosier (1993); 19, Fan et al (1992); 20, Mathews and Fung (1987); 21, Whalen and Reeburgh (1988); 22, Whalen and Reeburgh (1990); 23, Mosier et al (1996); 24, Sommerfeld et al (1993); 25, Houghton et al (1992); 26, Steudler et al (1989); 27, Crill (1991); 28, Schmidt et al (1988); 29, Bowden et al (1990);

30, Brumme and Beese (1992); 31, Dugas et al (1997); 32, Baldocchi et al (1997); 33, Simpson et al (1995); 34, IPCC (1995); 35, Meyers et al (1996); 36, Mosier unpublished data;

37, Ham and Knapp (1997); 38, Ruimy et al (1995)

r~

34 R.L Lapitan, R Wanninkhof and A.R Mosier

Table 2 General characteristics of the current methods and corresponding formulations for calculating surface gas fluxes in terrestrial systems

Method Footprint a Maximum instrument Formulations for calculating

frequency response a'b gas flux (F)

a Adapted from Denmead (1994)

b Sampling rate for automated systems

infrequent wind gusts can induce counter-gradient transport of gases, heat, and water vapor, and which can provide the greatest uncertainty in flux gradient / Bowen ratio measurements over cropland (Finnigan, 1979; Shaw et al., 1983; Meyers and Paw U, 1987) and forest (Denmead and Bradley, 1987) systems Strong pulses in surface-air gas exchange due to episodic changes ( < l h ) in atmospheric conditions, such as a heavy downpour, may be neglected because of restricted measurements during the event Nitric oxide (NO) is particularly susceptible, with emission rates typically increasing immediately following small rainfall events (Martin, 1996) On the other hand, CH4 uptake rates in soil are not strongly affected by small, short-term changes in temperature and soil water status (Mosier et al.,

Methods for stable gas flux determination in aquatic and terrestrial systems 3 5

1996) Because of these periodic short-term events, the question remains on the minimum sampling period to consider in time averaging of measurements for effectively estimating the actual magnitude of flux for the specific gas species being studied On a larger temporal scale, appropriate time averaging has to separately consider daytime from nighttime measurements due to differential flux directions associated with different biological mechanisms governing gas (e.g., CO2) exchange (Denmead et al., 1996) and different daytime and nocturnal vertical mixing processes occurring in the atmospheric boundary layer (Merrill, 1985)

3 Recent developments in analytical methods

Gas chromatography has been and still is the main analytical method commonly employed in measuring gas concentration Its sensitivity (detection limit) depends on the type of detector installed characteristic of the gas being measured (Table 3) Gas chromatography and other commonly used analytical methods such as non-dispersive infrared analysis for measuring atmospheric gases were reviewed comprehensively by Crill et al (1995)

For methods, such as chambers and conditional sampling, that do not require fast response analytical devices for measuring instantaneous gas concentrations, gas chromatography is most appropriate for analyzing CO2, CH4 and N20 Sampling of gases using chambers can be automated for continuous monitoring of surface gas fluxes, including CH4 and N20 fluxes from rice paddies (Schtitz et al., 1989; Bronson et al., 1997) In conjunction with micrometeorological methods, however, fast-response analytical devices are required to measure gas concentrations Fan et al (1992) coupled a flame ionization detector (FID) detector with a tower- and an aircraft-based eddy correlation method for measuring CH4 fluxes, considering the small variance (< 5%) obtained between sensible heat fluxes taken at

20 Hz (sampling frequency of the sonic anemometer) and at 8 Hz (sampling frequency of the FID detector) They noted, however, potential underestimation of the actual magnitude of CH4 fluxes by 10% due to large noise in the signal and inadequate sensitivity of the detector to resolve < 0.1 ppbv CH4 concentrations

For CO2, open- and closed-path infrared (IR) analyzers provide adequate sensitivity and time constant for flux measurements using eddy correlation (Leuning and Moncrieff, 1990; Leuning and King, 1992), as well as flux gradient analysis (Denmead and Raupach, 1993; Wagner-Riddle et al., 1996a) For CH4 and N20, in ecosystems where surface-air exchange of these trace gases are low and instantaneous fluctuations of gas concentrations at very fine temporal resolution are required, tunable diode laser (TDL), and Fourier-transform infrared (FTIR) spectrometers offer high resolution and time constant to detect gas concentrations at pptv levels in a second or fraction of a second (Table 3) An overview of these advanced spectroscopic instruments can be found in the literature (Kolb et al., 1995) An example of a TDL currently being used for N20 measurements provides, at 10 Hz sampling rate, an instrument drift of <0.05 ppbv h ~ and precision of 2 ppbv and 0.1 ppbv at 0.1 s and 16 min averaging time, respectively (Wienhold et al., 1996) Detailed specifications and resolutions

of the current FTIR spectrometers employed in quantitative detection of trace gases are discussed by Galle et al (1994), Griffith (1996), and Jaakkola et al (1997) Another spectrometer called near-infrared diode lasers (NIRDL), with a detector based on high frequency modulation of the laser at a single wavelength (1.690 ~tm), showed high in situ

sensitivity to CH4 at _<1 Hz response time suitable for eddy correlation and other micrometeorological techniques (Hovde et al., 1993)

Table 3 Characteristics of the current analytical methods/instruments associated with each of measuring surface fluxes of CO2, CE4 and N20 Numbers in parenthesis and superscripted

al, chemiluminescence; ECD, electron capture detector; FID, flame ionization detector; GC, gas chromatograph; GFCIR, gas filter correlation infrared absorption analyzer; IR, infrared absorption spectroscopy; NDIR, non-dispersive infrared absorption; NIRDL, near-infrared diode laser; TDL, tunable diode laser; FTIR, fourier transform infrared spectroscopy

b 1, Desjardins et al (1993); 2, Gosz et al (1988); 3, Wagner-Riddle et el (1996a); 4, Edwards et al (1994); 5, Simpson et al (1995); 6, Wienhold et al (1994); 7, Wagner-Riddle et al

(1996b); 8, Komhyr et al (1983); 9, Rasmussen and Khalil (1981); 10, Steele et al (1987); 11, Wenthworth and Freeman (1973); 12, Desjardins and MacPherson (1989); 13, Galle et al

Methods for stable gas flux determination in aquatic and terrestrial systems 37

4 Current methods for measuring gas fluxes in terrestrial systems

in Table 2

Generally errors in flux measurements can be attributed to the chamber effects on perturba- tions of the natural conditions of the sampling site, modifications of the microclimate, pressure-induced gas flows in open chambers, and inhibiting effects of concentration build-up

in closed-chamber designs These problems and the mechanics of correcting them have been discussed in detail elsewhere (Denmead, 1979; Hutchinson and Mosier, 1981; Mosier, 1990; Hutchinson and Livingston, 1993; Livingston and Hutchinson, 1995) If all the necessary checks on the chambers have been secured, spatial average of gas fluxes obtained from several chambers aligned at grid points along the wind direction can provide area-wide estimates of surface flux density within the accuracy constrained by the magnitude of spatial variabilities

observed (Christensen et al., 1996; Galle et al., 1994) With this sampling design, very close

similarities between spatially-average, point measurements and integrated estimates of N20 fluxes taken by chamber and flux-gradient methods, respectively, were observed (Christensen

et al., 1996)

4.2 Micrometeorologicai techniques

Conventional ground-based micrometeorological methods provide one-dimensional (along the vertical axis) measures of gas, heat, and water vapor flux densities, and as such, assume absence of horizontal perturbations in the equilibrium exchange rates between the surface and the air Generally surface flux measurements are affected by wind speed and direction, thermal stratification, and atmospheric stability Profiles of wind velocity, air temperature, and humidity are required to derive the parameters of the gas flux equations including the

parameters describing the surface roughness length (zo), eddy diffusivities for momentum (Kin), heat (KH), and water vapor (Kv), latent (H) and sensible heat (~E) fluxes, and

atmospheric stability correction factors for momentum (~m) and heat (~h) transports Hence, the limitations in resolution and real-time continuous monitoring capability of existing micrometeorological instruments are as much a major consideration in these methods as the

3 8 R.L Lapitan, R Wanninkhof andA.R Mosier

sensitivity of analytical devices for accurately quantifying surface-atmosphere gas exchange The theories behind each method have been elaborated in the literature (Denmead et al., 1977; Webb et al., 1980; Baldocchi et al, 1988; Fowler and Duyzer, 1989; Businger and Oncley, 1990; Desjardins et al., 1993; Denmead, 1994; Lenschow, 1995) and, in summary, the general characteristics and equations for calculating gas fluxes are shown in Table 2 The gas concentrations (Cg) are assumed to be taken from air samples pre-conditioned (e.g., through pre-heated lines) to a standard pressure and temperature or expressed as a mixing ratio with dry air Otherwise, in situ measurements of Cg require corrections for atmospheric density variations due to H and ~,E (Wesely et al., 1989; Denmead, 1994)

4.2.1 Flux gradient

The flux gradient method calculates the flux of a gas species from measurements of its concentration at different heights from the surface Over bare soils or bodies of water (e.g

ocean, lake), wind speed (u) approaches zero at the soil or water surface but over vegetated

diffusivity of the gas species (Kg), corrected for ~m and ~h, Can be calculated from temperature and wind profile measurements (Paulson, 1970; Businger et al., 1971) Most applications of flux gradient technique were primarily on CO2 since commercially available, open- and/or closed-path infrared gas analyzers (Table 3) are adequately sensitive to detect profile differences in CO2 concentrations over cropland and forest systems (Denmead and Raupach,

1993; Wagner-Riddle et al., 1996a; Meyers et al., 1996) Most recently, flux gradient applica-

Figure 1 Daily average N20 fluxes (Bowen ratio method) measured over a bare soil The arrows represent

a See Table 3; CS, conditional sampling; EC, eddy correlation; FG, flux-gradient

b 1, Edwards et al (1994) as cited in Wagner-Riddle et al (1996b); 2, Rudd et al (1993) as cited in Wagner-Riddle et al (I996b); 3, Simpson et al (1995), as cited in Wagner-Riddle et al (1996b); 4, Wagner-Riddle et al (1996b); 5, Hargreaves

et al (1996); 6, Christensen et al (1996)

tions have been extended for continuous (_< lh interval) flux determinations of CH4 (Wienhold

et al., 1994; Wagner-Riddle et al., 1996a,b) and N20 (Wienhold et al., 1994; Christensen et al., 1996; Wagner-Riddle et al., 1996a,b) from profile concentrations measured with TDL and FTIR spectrometers (Hargreaves et al., I996; Christensen et al., 1996) TDL-based flux gradient (FG-TDL) method sensitively quantified the minima and maxima of N20 fluxes from non-event and episodic conditions (rainfall or substrate addition) continuously over a bare soil surface (Figure 1) Over vegetated surfaces, FG-TDL measurements of N20 flux closely agreed with FTIR-based flux gradient (FG-FTIR) measurements (Table 4)

In summary from all these recent studies, the uncertainties in flux gradient estimates of gas fluxes are dictated by the accuracy of Kg estimates and existing spatial heterogeneity observed

in the sampling area Estimating Kg following the wind profile approach evidently alleviates the deficiencies of the energy balance method (see section 4.2.4) The high sensitivities of TDL and FTIR not only made CH4 and N20 flux measurements feasible using the flux gradient approach but also reduce the fetch requirement of the method Gas sampling height intervals of <lm dramatically reduce fetch requirements to _<100 m (Wagner-Riddle et al.,

1996a; Hargreaves et al., 1996), in contrast to earlier suggestions of 2-3 m sampling height intervals and fetch of >200 m to be able to detect small atmospheric gradients in gas concentrations (Denmead and Raupach, 1993) Reduction in sampling height from the surface also minimizes the potential errors associated with varying horizontal length scales (Lenschow, 1995)

4.2.2 Eddy correlation

Eddy correlation method relates the covariance of the instantaneous vertical wind velocity with the instantaneous fluctuations of gas concentration in the air, and as such, provides a direct measure of the vertical flux density of the gas from (or to in case of deposition) the underlying surface While it is the most direct of all the micrometeorological methods for measuring gas fluxes, it can be the most difficult and technically demanding to operate considering the rigorous management of instrument operations before and during data collection These include checks on instrument drifts, transient errors, vertical alignment, and siting geometry relative to the mast and other sensors (Businger, 1986) Also crucial in eddy

40 R.L Lapitan, R Wanninkhof and A.R Mosier

correlation set-up are the sensor sampling height and fetch considerations The minimum sensor (wind and temperature) height of 2 m requires a fetch of 200 m (Denmead and Raupach, 1993) Decrease in gas concentration gradient with height and thermal stratification affect estimates of gas flux density Over a sampling length of time (15min - lh) determined suitably for averaging, the gas flux can be determined from the equation given in Table 2 More recent applications of eddy correlation coupled this approach to the fast response TDL and FTIR spectrometers that can sensitively measure trace concentrations of CH4 (Edwards et at., 1994; Simpson et al., 1995; Wagner-Riddle et al., 1996b) and N20 (Wagner- Riddle et al., 1996a; Hargreaves et al., 1996; Christensen et al., 1996) Table 4 shows the dynamics of the TDL-based eddy correlation method (EC-TDL) for precisely determining trace gas fluxes within the spatial variabilities of gas exchanges observed in different terrestrial systems With N20 fluxes in the cropland system, the EC-FTIR and the FG-based measurements were taken from the same mast and locations and thus, comparable The data represent true N20 fluxes, i.e., surface integrated N20 fluxes weighted against the flux footprint (discussed more in a later section) calculated for the method, and showed close agreement of true fluxes estimated by the flux gradient and eddy correlation methods The discrepancy between EC-TDL and EC-FTIR N20 fluxes was not accounted for by intrinsic (e.g calibration and transient errors) differences between TDL and FTIR devices; rather, to biases associated with sampling site differences (Hargreaves et al., 1996)

4.2.3 Eddy accumulation and conditional sampling

The eddy accumulation method, like the eddy correlation method, requires a fast-response wind sensor to monitor vertical wind velocity and, in addition, control the valves for switching air sampling and accumulation in the "up" (C +) and "down" ((7) reservoirs coinciding with the upward and downward drafts of wind, respectively Air is pumped into the

C + and U reservoirs at same rates proportional to the magnitude of vertical wind velocity (Desjardins, 1977) Because accumulated instead of instantaneous air samples are measured in the two reservoirs for concentrations of the desired gas species, slow-response high-resolution spectrometers can be used The relaxed eddy accumulation method operates on the same principles and field set-up as the eddy accumulation method, except that air sampling rates at the C + and C- reservoirs are held constant (instead of linearly proportional to magnitude of vertical wind velocity); thus, simplifying flux calculations At the end of a suitable sampling period (ts) the difference in the mean concentrations of the gas species (i.e., Cg + and Cg-) taken from the C + and C ~ reservoirs are used to calculate flux of the gas following the equations given in Table 2 The proportionality coefficient (b) is given by:

(Z" O"w) Relaxed eddy accumulation where e is the pump rate per unit vertical wind speed and ~w is the standard deviation of the vertical wind speed Generally, the coefficient (z) ranged between 0.56 to 0.60 and, based on field experiments, is minimally affected by changes in atmospheric stability and turbulence intensity (Businger and Oncley, 1990; MacPherson and Desjardins, 1991; Baker et al., 1992; Ham and Knapp, 1997)

Offsets in vertical wind velocity, mechanical failures of fast switches and flow rate circuitry, and low resolution of gas analytical instruments provide potential sources of errors

in these techniques (Hicks and McMillen, 1984) The latter factor especially limits the