ALBERT

Notes: Assessments of carbon (C) fluxes in the Arctic require detailed data on both how and why these fluxes vary across the landscape. Such assessments are complicated because tundra vegetation has diverse structure and function at both local and regional scales. To investigate this diversity, the Arctic Flux Study has used the eddy covariance technique to generate ecosystem CO2-exchange data along a transect in northern Alaska. We use an extant process-based model of the soil–plant–atmosphere continuum to make independent predictions of gross photosynthesis and foliar respiration at 9 of the sites along the transect, using data on local canopy structure and meteorology. We make two key assumptions: (i) soil respiration is constant throughout the flux measurement period, so that the diurnal cycle in CO2 exchange is driven by canopy processes only (except at two sites where a soil respiration–temperature relationship was indicated in the data); and (ii) mosses and lichens play an insignificant role in ecosystem C exchange, even though in some locations their live biomass exceeds 300 g m−2. We found that even with these assumptions the model could explain much of the dynamics of net ecosystem production (NEP) at sites with widely differing vegetation structure and moss/lichen cover. Errors were mostly associated with the predictions of maximum NEP; the likely cause of such discrepancies was (i) a mismatch between vegetation sampled for characterizing the canopy structure and that contained within the footprint of the eddy covariance flux measurements, or (ii) an increase in daytime soil and root respiration. Thus the model results tended to falsify our first assumption but not our second. We also note evidence for an actual reduction in NEP caused by water stress on warm, dry days at some sites. The model–flux comparison also suggests that photosynthesis may be less sensitive to low temperatures than leaf-level gas-exchange measurements have indicated.

Notes: There are two broad approaches to quantifying landscape C dynamics – by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques – which combine stock and flux observations with a dynamic model – improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil CO2 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)=−251±197 g C m−2 over the 3 years, compared with an estimate of −419±29 g C m−2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5 g C m−2 day−1, but the uncertainty on assimilated estimates averaged 0.47 g C m−2 day−1, and only exceeded 0.5 g C m−2 day−1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis – which might be provided indirectly by remotely sensed data – improves the analysis of NEE.

Notes: Abstract Mechanistic models of nutrient uptake are essential to the study of plant-soil interactions. In these models, uptake rates depend on the supply of the nutrient through the soil and the uptake capacity of the roots. The behaviour of the models is complex, although only six to ten parameters are used. Our goal was to demonstrate a comprehensive and efficient method of exploring a steady-state uptake model with variation in parameters across a range of values described in the literature. We employed two analytical techniques: the first a statistical analysis of variance, and the second a graphical representation of the simulated response surface. The quantitative statistical technique allows objective comparison of parameter and interaction sensitivity. The graphical technique uses a judicious arrangement of figures to present the shape of the response surface in five dimensions. We found that the most important parameters controlling uptake per unit length of root are the average dissolved nutrient concentration and the maximal rate of nutrient uptake. Root radius is influential if rates are expressed per unit root length; on a surface area basis, this parameter is less important. The next most important parameter is the effective diffusion coefficient, especially in the uptake of phosphorus. The interactions of parameters were extremely important and included three and four dimensional effects. For example, limitation by maximal nutrient influx rate is approached more rapidly with increasing nutrient solution concentration when the effective diffusion coefficient is high. We also note the ecological implications of the response surface. For example, in nutrient-limited conditions, the rate of uptake is best augmented by extending root length; when nutrients are plentiful increasing uptake kinetics will have greater effect.