Sensitivity and predictive uncertainty of the ACASA model at a spruce forest site

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Abstract

The sensitivity and predictive uncertainty of the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) was assessed by employing the Generalized Likelihood Uncertainty Estimation (GLUE) method. ACASA is a stand-scale, multi-layer soil-vegetation-atmosphere transfer model that incorporates a third order closure method to simulate the turbulent exchange of energy and matter within and above the canopy. Fluxes simulated by the model were compared to sensible and latent heat fluxes as well as the net ecosystem exchange measured by an eddy-covariance system above the spruce canopy at the FLUXNET-station Waldstein-Weidenbrunnen in the Fichtelgebirge Mountains in Germany. From each of the intensive observation periods carried out within the EGER project (ExchanGE processes in mountainous Regions) in autumn 2007 and summer 2008, five days of flux measurements were selected. A large number (20000) of model runs using randomly generated parameter sets were performed and goodness of fit measures for all fluxes for each of these runs were calculated. The 10% best model runs for each flux were used for further investigation of the sensitivity of the fluxes to parameter values and to calculate uncertainty bounds. A strong sensitivity of the individual fluxes to a few parameters was observed, such as the leaf area index. However, the sensitivity analysis also revealed the equifinality of many parameters in the ACASA model for the investigated periods. The analysis of two time periods, each representing different meteorological conditions, provided an insight into the seasonal variation of parameter sensitivity. The calculated uncertainty bounds demonstrated that all fluxes were well reproduced by the ACASA model. In general, uncertainty bounds encompass measured values better when these are conditioned on the respective individual flux only and not on all three fluxes concurrently. Structural weaknesses of the ACASA model concerning the soil respiration calculations and the simulation of the latent heat flux during dry conditions were detected, with improvements suggested for each. © 2010 Author(s).

Figures

  • Fig. 1. Vertical profiles of the cumulative (a) and absolute (b) plant area index (PAI) at the Waldstein-Weidenbrunnen site (May 2008). Profiles are mean values of five measured PAI profiles with the corresponding standard deviations indicated. The dashed line in (b) represents the fitted PAI profile for the ACASA model (weighted sum of two beta distributions fitted to the measured data following Simon et al., 2005, 101 data points).
  • Table 1. Meteorological input parameters of the ACASA model and the corresponding measurements at the Waldstein-Weidenbrunnen site.
  • Fig. 2. Meteorological conditions during the two five day periods (left: IOP-1, right: IOP-2). (a) and (b): Global radiation (solid line) and air temperature (dotted line) above the canopy (30 m and 31 m). (c) and (d): Vapor pressure deficit above the canopy (31 m). (e) and (f): Wind speed above the canopy (31 m). (g) and (h): Soil temperature (solid line) and soil moisture (dotted line) at 10 cm depth.
  • Table 2. List of the external (first 16) and internal (plant physiological, second 8) input parameters to the ACASA model which were studied in the sensitivity analysis, the range over which each parameter was varied and the reference values for the ACASA as well as the PSN6 model for our site.
  • Fig. 3. Sensitivity graphs showing the range of the single-objective coefficients of efficiency for the best 10% parameter sets (left: IOP-1, right: IOP-2) for the sensible heat flux, H, (a) and (b), the latent heat flux, LE, (c) and (d), and the NEE, (e) and (f), across the range of the leaf area index, lai [m2 m−2]. The vertical dashed line denotes the reference parameter value. Cumulative frequencies are plotte in (g) and (h) for the three fluxes as well as for the combined likelihood measure with the diagonal solid line showing a uniform parameter distribution for comparison.
  • Table 3. Maximum and minimum values of the coefficient of efficiency E for sensible heat flux (H), latent heat flux (LE), ground heat flux (G), net ecosystem exchange (NEE), short-wave radiation budget (Rn(sw)), long-wave radiation budget (Rn(lw)) and for net radiation (Rn) for IOP-1 and IOP-2 (20 000 runs each).
  • Fig. 4. Scatter plots of the coefficients of efficiency for the three fluxes. Each dot represents one parameter set. (a)–(c): Individual coefficients of efficiency for IOP-1 compared to each other. (d)–(f): Same as (a)–(c) but for IOP-2. (g)–(i): For each flux, coefficients of efficiency compared for the two IOPs. Note the differences in the axis ranges.
  • Fig. 5. Cumulative likelihood distributions for the model parameters q10s, Q10 for stem respirat [−], (a) and (b), iqe, quantum efficiency [−], (c) and (d), and r0m, microbe basal resp. rate at 0 ◦C [µmol m−2 s−1], (e) and (f), for the 10% best parameter sets for the single-objective and combined coefficients of efficiency (left column: IOP-1, right column: IOP-2). The thin black diagonal represents a uniform parameter distribution. In (a), (b), (e) and (f) the dashed vertical line depicts the reference parameter for the Waldstein-Weidenbrunnen site. In (c) and (d) the dashed vertical line shows the original ACASA parameter whereas the dotted vertical line depicts the reference value of the PSN6 model for our site.

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CITATION STYLE

APA

Staudt, K., Falge, E., Pyles, R. D., Paw U, K. T., & Foken, T. (2010). Sensitivity and predictive uncertainty of the ACASA model at a spruce forest site. Biogeosciences, 7(11), 3685–3705. https://doi.org/10.5194/bg-7-3685-2010

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