The effects of constraining variables on parameter optimization in carbon and water flux modeling over different forest ecosystems

Abstract

a b s t r a c tThe ability of terrestrial biogeochemical models in predicting land-atmospheric carbon and water exchanges is largely hampered by the insufficient characterization of model parameters. The direct observations of carbon/water fluxes and the associated environmental variables from eddy covariance (EC) flux towers provide a notable opportunity to examine the underlying processes controlling carbon and water exchanges between terrestrial ecosystems and the atmosphere. In this study, we applied the Metropolis simulated annealing technique to conduct parameter optimization analyses of a processbased biogeochemical model, simplified PnET (SIPNET), using a variety of constraining variables from EC observations and leaf area index (LAI) from MODIS at three ChinaFLUX forest sites: a temperate mixed forest (CBS), a subtropical evergreen coniferous plantation (QYZ) and a subtropical evergreen broadleaved forest (DHS). Our analyses focused on (1) identifying the key model parameters influencing the simulation of carbon and water fluxes with SIPNET; (2) evaluating how different combinations of constraining variables influence parameter estimations and associated uncertainties; and (3) assessing the model performance with the optimized parameterization in predicting carbon and water fluxes in the three forest ecosystems. Our sensitivity analysis indicated that, among three different forest ecosystems, the prediction of carbon and water fluxes was mostly affected by photosynthesis-related parameters. The performances of the model simulations depended on different parameterization schemes, especially the combinations of constraining variables. The parameterization scheme using both net ecosystem exchange (NEE) and evapotranspiration (ET) as constraining variables performed best with most well-constrained parameters. When LAI was added to the optimization, the number of well-constrained model parameters was increased. In addition, we found that the model cannot be well-parameterized with only growingseason observations, especially for those forest ecosystems with distinct seasonal variation. With the optimized parameterization scheme using both NEE and ET observations all year round, the SIPNET were able to simulate the seasonal and inter-annual variations of carbon and water exchanges in three forest ecosystems.

Modeling forest carbon cycle using long‐term carbon stock field measurement in the Delaware River Basin

“…, Liu et al. ). The eddy covariance data sets consist of fine time resolution (hourly observations), but are very low in their spatial resolution (i.e., few sites covering a large area).…”

“…In most modeling studies of forest C cycles, models are parameterized or calibrated using carbon fluxes measurements from eddy covariance techniques from one or several flux towers (Carvalhais et al 2010, Liu et al 2015. The eddy covariance data sets consist of fine time resolution (hourly observations), but are very low in their spatial resolution (i.e., few sites covering a large area).…”

Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach

“…Process-based terrestrial ecosystem models are important tools for studying key processes of C cycles and the mechanisms underlying their control and have been widely used to estimate regional and/or global C budgets [7][8][9][10][11]. However, given the complexity of ecosystems, our current understanding of ecosystem-related key processes and control mechanisms is insufficiently comprehensive, as model parameters inevitably have associated uncertainties, and thus these models are still unable to accurately simulate and predict ecosystem processes and C source/sink distribution and changes [12][13][14][15]. In this regard, the model-data fusion technique (MDF) provides a powerful tool for reducing the uncertainty when simulating ecosystem C cycles by combining observations and models, which can contribute to improving model simulation accuracy [16][17][18].…”

“…MDF makes full use of existing observations and applies mathematical methods to optimize the parameters and/or state variables of the model to achieve the best combination between simulations and measurements, thereby enabling more accurate simulation of the changes in ecosystem state [19][20][21]. The accuracy of these simulations, in turn, depends on the appropriate acquisition of model parameters [2,12,[22][23][24] and, given that models contain a large number of parameters that are difficult to estimate accurately, most of the research on MDF in the field of C cycles have tended to focus on parameter estimation [9,12,15,25,26].…”

“…At present, various time scales and types of observational data are commonly used in terrestrial process-based terrestrial ecosystem modeling, which may contain different types of information on ecosystem processes. For example, C flux data contains considerable amounts of information regarding how "fast" processes respond to environmental drivers (e.g., photosynthesis and respiration), whereas biometric data contains information relating to numerous "slow" processes (e.g., C pool size and turnover) [1,15,27,28]. Therefore, the data used as constraints require multiple data streams.…”