A strategy for the regional analysis of the effects of physical and chemical climate change on biogeochemical cycles in northeastern (U.S.

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1 Ecological Modelling, 67 (1993) Elsevier Science Publishers B.V., Amsterdam 37 A strategy for the regional analysis of the effects of physical and chemical climate change on biogeochemical cycles in northeastern (U.S.) forests John D. Aber a, Charles Driscoll b, C. Anthony Federer c, Richard Lathrop d, Gary Lovett e, Jerry M. Melillo f, Paul Steudler f and James Vogelmann a a Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH, USA b Department of Civil Engineering, Syracuse University, Syracuse, NY, USA c Northeastern Forest Experiment Station, U.S. Forest Service, Mast Road, Durham, NH, USA d Department of Environmental Resources, Rutgers University, New Brunswick, NJ, USA e Institute of Ecosystem Studies, Mary Flagler Cary Arboretum, Millbrook, NY,, USA f The Ecosystem Center, Marine Biological Laboratory, Woods Hole, MA, USA ABSTRACT Aber, J.D., Driscoll, C., Federer, C.A., Lathrop, R., Lovett, G., Melillo, J.M., Steudler, P. and Vogelmann, J., A strategy for the regional analysis of the effects of physical and chemical climate change on biogeochemical cycles in northeastern (U.S.) forests. EcoL Modelling, 67: A method is presented for extrapolating the results of site-level ecosystem studies to regional scales. Simple, data-intensive models of ecosystem function are combined with regional data planes describing physical and chemical climate to yield regional predictions. The importance of validating regional predictions with rigorous regional measurements is stressed. Examples of available models and validation data sets are presented. INTRODUCTION Many of our most pressing environmental concerns, such as climate change and atmospheric deposition, are regional to global, rather than local, in extent. Our ability to understand the impact of these perturbations on managed and wild ecosystems is limited by the limited spatial extent Correspondence to: J.D. Aber, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH 03824, USA /93/$ Elsevier Science Publishers B.V. All rights reserved

2 38 J.D. ABER ET AL. (plots to watersheds) of most ecosystem research. It is unlikely that the resources will ever be available to conduct intensive ecosystem research at enough sites within any one region to allow direct statistical analysis of ecosystem-pollution interactions. Large-scale climate experiments are simply not possible. Combining computer models, spatial data bases and remotely sensed data within a geographic information system (GIS) has been proposed as a method of extrapolating site-level research to regional scales (e.g. Running and Nemani, 1988; Burke et al., 1990; Schimel et al., 1990). While these tools increase the speed and precision with which regional extrapolations may be made, the accuracy of the results generated depends on the validity of the models used to relate the spatial data sets to ecosystem function. This in turn relies on a close integration of high-quality field data with the ecosystem- and spatial-modeling efforts. The purposes of this paper are: (1) to outline one approach to the integration of field data with modeling, GIS and remote sensing techniques, (2) to discuss a modular approach to the construction of a complex model of ecosystem biogeochemistry, and (3) to discuss how such approaches are being applied to predicting the regional response of forest ecosystems in the northeastern USA to changes in climate and atmospheric deposition. COMPONENTS OF RESEARCH AT THE REGIONAL SCALE Research generally proceeds through cycles of measurement, data analysis and synthesis, with synthesis leading to the generation of additional hypotheses to be tested by measurement. For studies conducted at the plot scale (top right-hand side of Fig. 1), if the synthesis occurs in the form of a computer model and the environmental drivers for this model are available or can be generated from regional data bases, then the model can be used directly for regional predictions. However, without validation, those predictions are actually only additional hypotheses expressed at a much larger scale (so that the "regional predictions" and "regional hypotheses" boxes are actually the same). Validation (the comparison of model predictions with data not used in constructing or calibrating the model) requires actual measurement at regional scales, or across dominant environmental gradients within the region (top left-hand side of Fig. 1). Two points can be made from this integration of site- and regional-level research. The first is that models occupy a pivotal position between site and regional scales. Models provide the vehicle for synthesizing data from the many disciplines required to address critical questions, for generating

3 REGIONAL ANALYSIS OF EFFECTS OF CLIMATE CHANQE 39 INTEGRATING REGIONAL- AND SITE-LEVEL RESEARCH..... i REGIONAL SCALE HYPOTHESES r II IZ... REGIONAL i I SAMF'UNG UODELS "- i "! SITE-LEVEL i HYPOTHESES /- -,, - SITE-LEVEL SAMPLING i, REGIONAL ~ in,-,-, o^o=o! D GEOGRAPHIC....~,... ] ~ INFORMATION' i i~emote S SYSTEM - SENSING REGIONAL! ~! PROJECTIONS I Fig. 1. Schematic diagram of the interaction between site-level research, regional-level research, regional data bases, computer models and geographic information systems in the analysis of regional responses to altered physical and chemical climate. hypotheses at larger scales, and predicting responses to proposed management scenarios. The second is that the acquisition of high-quality, extensive, regionalscale data sets is as important in the validation of models applied at the regional scale, as intensive site data are in deriving those models. It can be very difficult to obtain support for such regional sampling schemes, as the large number of sites which must be sampled allows for only simple, summary measurements to be made at each site. Such extensive sampling might appear superficial relative to the intensity and frequency with which sampling can be carried out in a single intensive study site. However, it is crucial to demonstrate the accuracy with which a model can be applied to regional questions. A coherent method for obtaining validation data through extensive sampling should be a central component of any regional analysis. Remote sensing provides a unique tool for measuring key attributes of ecosystems over large areas. It can provide either basic data planes to a geographical information system (e.g. the use of Thematic Mapper data to determine vegetation type or land cover) or can provide validation data (e.g. AVHRR data on seasonality and amount of green plant biomass production).

4 40 J.D. ABER ET AL. COMPONENTS OF A REGIONAL BIOGEOCHEMICAL MODEL Models of ecosystem function are becoming increasingly complex as our understanding of function increases. A complete biogeochemical model of terrestrial ecosystems can be conceived as including several modules centered on the broad, traditional disciplines from which they draw (Fig. 2). To date, most of the ecosystem models which have been produced have been monolithic, in that they are fully integrated. They are not constructed from separate modules which can be interchanged, nor are they intended to serve as modules in a larger model. Given the complexity within each "box" and the degree of cooperation between groups required to produce such a complete model, the tendency has been, rather, to produce models which are very strong in one area (such as production and nutrient cycling), and quite weak in another (such as soil chemistry or hydrology). We are taking a strongly modular approach to the construction of the model outlined in Fig. 2. By clearly specifying which variables must be passed between modules (e.g. Table 1), the internal structure of each module then becomes independent of the others, and can be developed separately. It is also possible to develop several different algorithms for each process which might be appropriate for answering different kinds of MODULAR GLOBAL CHANGE MODELING SYSTEM BIOTIC CHARACTERIZATION CLIMATE ~J PRODUCTION ]~ SOIL DRIVERS ~.~ NUT. CYCLIN_G~ PROPERTIES HYDROLOGY ~ l r ~ \ ~ ~ SOIL I PHOTOSYN, j ~ ~ \f--~ I CHEMISTRY I ~ BALANCE ) I SOLUTION J M O D E L ~. ~ f--\ ~\ ATMOSPHERIC '~._(/~EERN/~ DEPOSITION Fig. 2. Outline of a modular approach to the construction of a model of ecosystem biogeochemistry. Boxes represent the program modules. Circles indicate information produced by, and used as inputs by, the different modules. Unenclosed text represents external data required to run the model.

5 REGIONAL ANALYSIS OF EFFECTS OF CLIMATE CHANGE 41 TABLE 1 Information required of, and passed between, modules of the biogeochemical model (Fig. 2) Passed from Passed to Hydrology/ Production/ Soil Trace photosynthesis nutrient cycling chemistry gas Hydrology/ canopy carbon soil water soil water photosynthesis balance (net content content photosynthesis) Production/ leaf area and element uptake nutrient cycling chemistry (plant growth) and release (decomposition) Soil chemistry element soil solution availability chemistry (for uptake) Trace gas gaseous losses from soils questions. Modularity increases the ease with which one algorithm can be substituted for another within the model. The three models currently being linked in this way are: (1) VEGIE (Aber et al., 1991), a model predicting NPP, nutrient uptake and the allocation of NPP and nutrients in relation to the availability of nutrients, water and light; (2) MANE (Santore and Driscoll, 1992), a multiphase aqueous geochemistry equilibrium model, and (3) BROOK90, a revised version of the BROOK forest hydrology model (Federer and Lash, 1978), first developed for use at Hubbard Brook, and now widely applied to both forest and non-forest ecosystems. A fourth model dealing with environmental controls over trace gas fluxes (particularly CH 4 and N20) is currently being developed. A critical component of these models is that they can be run using variables available from extensive data sets (see next section), or those that can be derived from such data sets. For some applications, simple models operating at longer time steps and requiring fewer inputs may be preferable to more complex and data-demanding models. For example, we have developed a simple monthly-time-step model of both water and carbon balances which captures both the seasonal and annual changes in transpiration, water storage and net photosynthesis, and r~quires only data available form the regional data bases described below (Aber and Federer, 1992).

6 42 J.D. ABER ET AL. APPLICATION TO BIOGEOCHEMISTRY OF NORTHEASTERN U.S. FORESTS The northeastern USA (Fig. 3) is now a heavily forested region adjacent to and including areas of heavy industrialization and high population density. Between the time of European settlement and the mid-1800s, as much as 80% of the area in southern New England was deforested and converted to croplands and pastures. Other parts of the region were deforested to a lesser extent. The major forces currently driving change in ecosystem function within this region include alterations in atmospheric deposition as a result of fossil fuel combustion and industrial activity (Reuss and Johnson, 1986; Gorham, 1989; Likens 1989; McNulty et al., 1990), the potential for climate change in the global context (Mitchell et al., 1990), and changes in the patterns of human use of the landscape. These forces are driving concern for effects as diverse as degradation of water quality (Nilsson and Grennfelt, 1988; Malanchuk and Nilsson, 1989), loss and fragmentation of wildlife habitat (Ambuel and Temple, 1983; DeGraaf and Healy, 1989), and production and consumption of radiatively active trace gases (Steudler et al., 1989), among others. Beginning with available digital data sets and remote sensing images, we are compiling data planes of important drivers for a regional biogeochemical model (Fig. 4). First order data sets include a digital elevation model (DEM), and level 1 land use/land cover data provided by the U.S. Geological Survey (Anderson et al., 1976; U.S. Geological Survey, 1987), as well as soil survey information provided by the U.S. Soil Conservation Service (STATSGO). All three of these are at a scale of 1 : , and will allow 80-m resolution (pixel size) within the GIS. From these data sets alone, considerable information on interactions between topography, soils and land use can be obtained (e.g. Fig. 5). Land use/land cover data are also being updated using recent AVHRR images (such as Fig. 3). An atmospheric wet deposition model based on relationships between latitude, longitude, elevation and the amount and chemistry of precipita- Fig. 3 (top). AVHRR scene of the region included in this analysis. The area reaches from the Adirondack Mountains in eastern New York state, across New England to eastern Maine. Fig. 5 (bottom). Elevation and land use data are combined to generate this image of Mt. Washington in northern New Hampshire. The top of the mountain is above tree line and supports alpine tundra vegetation. Moving down slope the vegetation changes to spruce-fir, mixed and deciduous forest zones. Limited areas of agricultural and "urban" use (mainly parking areas) are also visible.

7 REGIONAL ANALYSIS OF EFFECTS OF CLIMATE CHANGE 43

8 44 J.D. ABER ET AL. GIS SOURCE U.S.G.S. U.S.G.S STATSGO DERIVED DERIVED DERIVED DATA PLANES ELEVATION / LAND USE/COVER / Fig. 4. Basic data planes to be used in the GIS. Elevation, land use/land cover, and soils data are available in digital form. Precipitation, temperature and atmospheric deposition data planes are being generated from regional point data sets. tion received has been derived. Precipitation data are available from over 300 stations, and precipitation chemistry data from 26 National Atmospheric Deposition Program (NADP) sites throughout the region (Ollinger et al., 1993). Available data generally come from sites below 600 m elevation. Predictions above this elevation rely on relatively sparse data sets collected as part of scientific studies (e.g. Lovett and Kinsman, 1990) rather than through monitoring networks. A similar approach is being used to develop models of mean monthly maximum and minimum temperatures driven by latitude, longitude and elevation, and solar radiation as a function of latitude, slope and aspect. To this point, validation of derived relationships is not hindered by a lack of data, and traditional statistical techniques can be employed to determine their precision. VALIDATING THE BIOGEOCHEMICAL MODEL The data planes listed above constitute the climate drivers and soil properties which serve as inputs to the biogeochemical model. However, before the model can be applied to the prediction of the effects of altered climate or atmospheric deposition, it must be well validated against inde-

9 REGIONAL ANALYSIS OF EFFECTS OF CLIMATE CHANGE 45 pendent field data. Very few studies have collected data relevant to all four sets of processes in the model system. We can, however, validate combinations of modules against field data where available. For example, the Hubbard Brook Ecosystem Study (Likens et al., 1977; Bormann and Likens, 1979) has acquired detailed data sets on the response of northern hardwood forest ecosystems to a variety of experimental manipulations, including commercial clearcutting, whole-tree harvesting and complete devegetation. Data are available on quantity and chemical quality of stream flow, as well as plant biomass and nutrient accumulation through time. A combination of the hydrology, biology and soil chemistry models is currently being tested against this rich data base. If this validation is successful, then the model can be applied to questions of the effects of different atmospheric deposition scenarios (e.g. the effects of the Clean Air Act of 1990), different management practices (e.g. whole-tree harvesting versus clear cutting), and combinations of the two. To the extent that the GIS can be used to identify areas of similar vegetation, soils and climate, those predictions can be applied spatially across the region. Without the soil chemistry module, the production and hydrology modules can be applied to questions of climate change. In particular, once validated in terms of water balances and NPP, this module combination can be run with different climate change scenarios to determine effects on water yield and on vegetation water stress and NPP (e.g. Abet et al., 1991). At the regional scale, field validation data have been collected on the changes in N mineralization and nitrification, and soil and foliar chemistry which occur in spruce-fir forests along an atmospheric deposition gradient from eastern New York state to eastern Maine. These show significant increases in soil and foliar N content as well as N cycling rates with increasing deposition, and decreasing lignin concentration in foliage (Mc- Nulty et al., 1990, 1991). A critical finding is a relationship between forest floor N concentration and net nitrification rate. The VEGIE model is currently being parameterized with data on spruce-fir growth and decomposition. This version will be driven with regional deposition data to determine whether the measured soil and foliar patterns can be reproduced. If this validation is successful, then the model can be applied to the questions of N retention capacity and the timing and extent of nitrogen saturation (Aber, 1992), and eventually nitrate leaching losses, from this forest type under different N deposition scenarios. Similar validation exercises can occur where intensive watershed or plot level research has been conducted, or where extensive regional data sets are available. We have proposed to build on the spruce-fir gradient work by carrying out similar regional-scale sampling regimes for soil and foliar chemistry within the northern hardwood forest type, and for stream water

10 46 J.D. ABER ET AL. chemistry throughout the region. It is critical that the model be validated in a way that is relevant to the particular application at hand. CONCLUSION Extending ecosystem analysis from the site level to the regional level will require expertise in many disciplines. Geographic information systems may be used to synthesize spatial relationships, while computer models synthesize functional relationships. The combination of these two allow application of existing knowledge to critical regional environmental questions. Models will be required that can be run using only the types of information available from extensive data sets such as those listed in Fig. 4. The availability of extensive, regional-scale field-collected data sets will be critical for the validation of models at this scale. ACKNOWLEDGEMENTS This work is supported in part by the National Science Foundation through the Long-Term Ecological Research Program grant to the Harvard Forest. We thank Jennifer Ellis for operation of the geographic information system. REFERENCES Aber, J.D., Nitrogen cycling and nitrogen saturation in temperate forest ecosystems. Trends Ecol. Evolut., 7: Aber, J.D. and Federer, C.A., A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in forest ecosystems. Oecologia, 92: Aber, J.D., Melillo, J.M., Nadelhoffer, K.J., Pastor, J. and Boone, R., Factors controlling nitrogen cycling and nitrogen saturation in northern temperate forest ecosystems. Ecol. Appl., 1" Ambuel, B. and Temple, F.A., Area dependent changes in the bird communities and vegetation of southern Wisconsin forests. Ecology, 64: Anderson, J.R., Hardy, E.E., Roach, J.T. and Witmer, R.E., A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper 964, 28 pp. Bormann, F.H. and Likens, G.E., Pattern and Process in a Forested Ecosystem. Springer-Verlag, New York. Burke, I.C., Schimel, D.S., Yonker, C.M., Parton, W.J., Joyce, L.A. and Lauenroth, W.K., Regional modeling of grassland biogeochemistry using GIS. Landscape Ecol., 4: DeGraaf, R.M. and Healy, B., Forest fragmentation: A management issue in the northeast U.S. Forest Service Report NE-140. Federer, C.A. and Lash, D., BROOK: A hydrologic simulation model for eastern forests. Univ. New Hamp. Water Resource Res. Cent. Rep. 19.

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