Inverse modeling of seasonal drought effects on canopy CO₂/H₂O exchange in three Mediterranean ecosystems

Abstract

[1] We present a two-criteria inverse modeling approach to analyze the effects of seasonal drought on ecosystem gas exchange at three Mediterranean sites. The three sites include two nearly monospecific Quercus ilex L. forests, one on karstic limestone (Puéchabon), the other on fluvial sand with access to groundwater (Castelporziano), and a typical multispecies shrubland on limestone (Arca di Noè). A canopy gas exchange model Process Pixel Net Ecosystem Exchange (PROXEL NEE ), which contains the Farquhar photosynthesis model coupled to stomatal conductance via the Ball-Berry model, was inverted in order to estimate the seasonal time course of canopy parameters from hourly values of ecosystem gross carbon uptake and transpiration. It was shown that an inverse estimation of leaf-level parameters was impossible when optimizing against ecosystem H 2 O or CO 2 fluxes alone (unidentifiable parameters). In contrast, a criterion that constrained the optimization against both H 2 O and CO 2 fluxes yielded stable estimates of leaf-level parameters. Two separate model inversions were implemented to test two alternative hypotheses about the response to drought: a reduction in active leaf area as a result of patchy stomatal closure or a change in photosynthetic capacities. In contrast to a previously tested hypothesis of classical (uniform) stomatal control, both hypotheses were equally able to describe the seasonality of carbon uptake and transpiration on all three sites, with a decline during the drought and recovery after autumn rainfall. Large reductions of up to 80%, in either active leaf area or photosynthetic capacities, were necessary to describe the observed carbon and water fluxes at the end of the drought period. With a threshold-type relationship, soil water content was an excellent predictor of these changes. With the drought-dependent parameter changes included, the canopy model explains 80-90% of the variance of hourly gross CO 2 uptake (root mean squared error (RMSE): 1.1-2.6 mmol m À2 s À1 ) and 70-80% of the variance of hourly transpiration (RMSE: 0.02-0.03 mm h À1 ) at all sites. In addition to drought effects, changes in leaf photosynthetic activity not related to water availability, i.e., high spring activity, were detected through the inverse modeling approach. Moreover, our study exemplifies a kind of multiconstraint inverse modeling that can be profitably used for calibrating ecosystem models that are meant for global applications with ecosystem flux data.

Linking flux network measurements to continental scale simulations: ecosystem carbon dioxide exchange capacity under non-water-stressed conditions

“…Nevertheless, consensus views on processing of data from such networks are being developed to (1) provide estimates of the flux components associated with canopy photosynthesis [gross primary production (GPP)] and ecosystem respiration (Reco) (cf. Falge et al, 2001Falge et al, , 2002aReichstein et al, 2005), (2) reveal seasonal changes in CO 2 exchange potentials (Falge et al, 2002b;Reichstein et al, 2002;Gilmanov et al, 2003), and (3) support the derivation of model parameters for use in spatial generalizations of vegetation/atmosphere CO 2 exchange (Reichstein et al, 2003b;Wang et al, 2003).…”

Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2

“…Unlike other commonly used models [Wang et al, 2001;Reichstein et al, 2003;Knorr and Kattge, 2005;Wang et al, 2007], their model explicitly tracked changes in aboveground and soil carbon pools, thus potentially capturing the dynamics of longer-term processes such as forest succession that influence carbon fluxes over decadal to centennial scales. While their predictions for daily to annual net carbon fluxes improved significantly, their optimized model gave rise to unrealistic long-term carbon dynamics with excessive rates of carbon sequestration in vegetation and excessive decomposition of soil carbon stocks.…”

“…In particular, several studies have used assimilation techniques to incorporate flux tower measurements into models. Wang et al [2001] used three weeks of eddy-flux measurements to estimate photosynthesis and stomatal conductance parameters of a simplified terrestrial biosphere model designed to predict seasonal-to-interannual carbon fluxes, but found that site-specific model parameters were required to match the observations; similarly, Reichstein et al [2003] optimized an ecosystem model using CO 2 and H 2 O fluxes from Mediterranean ecosystems, but also found that site-and season-dependent model parameters were required. It appears that simplified biosphere and ecosystem models may not be able to make reliable predictions for locations and time periods other than those used in the model fitting.…”

Influences of observation errors in eddy flux data on inverse model parameter estimation

“…The study of Trudinger et al (2007) showed that how data errors and uncertainties are treated in the optimization criterion will have a significant impact on the retrieved parameters. Studies using EC data in inverse modelling often assume constant error variance (Reichstein et al, 2003;Owen et al, 2007;Wang et al, 2007), use the standard deviation of the model residuals (Sacks et al, 2006;Braswell et al, 2005) or an adhoc fraction of the observations (Knorr and Kattge, 2005). During the last few years approaches for the quantification of random errors of EC data came up, they used paired observations, first spatially separated measurements (Hollinger et al, 2004), but as there are only few appropriately distanced towers available, Hollinger and Richardson (2005) developed a methodology using daily differenced measurements with equivalent environmental conditions that allowed to characterize the univariate distribution for several sites Richardson et al, 2006).…”