小時(shí)步長(zhǎng)森林碳循環(huán)模型(BEPS)參數(shù)優(yōu)化及應(yīng)用研究
[Abstract]:Forest is the main part of terrestrial ecosystem, and forest carbon cycle plays an important role in terrestrial carbon cycle system. This study optimized and validated the hourly carbon cycle model BEPS Hourly at site scale, and explored the synergistic application of hourly carbon cycle model and daily carbon cycle model at regional scale. Firstly, the driving and validating data of BEPS Hourly model were collected at site scale (flux tower effective coverage area), and the main photosynthetic parameters, maximum carboxylation rate (Vc max) and maximum electron transfer rate (Umax), were optimized by iterative method. The biomass allocation parameters of different forest types in BEPS Hourly model were optimized, and the radiation transfer model was used as a breakthrough point to optimize the radiation transfer model considering the secondary canopy penetration and multiple reflectance, so as to improve the simulation ability of BEPS Hourly model for latent sensible heat flux on the basis of more accurate calculation of net radiation. The data assimilation algorithm-EnKF was used to correct the soil moisture predicted by BEPS Hourly model to reduce the cumulative error with time; the optimized BEPS Hourly model was used to simulate and validate the soil temperature, and the change of snow depth was simulated and validated, and the canopy temperature was simulated. Leaf area index (LAI) and meteorological factors (temperature, precipitation, wind speed, total solar radiation, relative humidity) were quantitatively analyzed. At the regional scale, the regional driving data of BEPS Daily model were collected. Because the existing MODIS LAI products could not meet the application requirements, the time series suitable for small and medium-sized regional scale were developed. Study on the method of fast LAI estimation in woodland; study the characteristics and applicability of BEPS Daily and BEPS Hourly, especially analyze the transfer reliability of main photosynthetic parameters Vcmax, Jmax from BEPS Hourly model to BEPS Daily model. On the basis of reliability analysis of parameter transfer, the BEPS Hourly model and BEPS Daily model are established. Based on the optimized BEPS Hourly, the intraday variation of GPP and NPP was simulated and analyzed at the site scale, and the regional NPP estimation and carbon source/sink spatial analysis were carried out based on the optimized BEPS Daily model. When L.m-2.s-1 was used, the RMSE of daily GPP based on BEPS Hourly simulation was the smallest, 1.10 g C.m-2.d-1, R2 was the highest, 0.95. After optimization of photosynthetic parameters Vc Max and Jmax, the BEPS Hourly model could better simulate the seasonal variation of GPP. 2. Broadleaf forest accounted for 4% of the total biomass and branches accounted for 4% of the total biomass. 96% of the total biomass, 5% of the total aboveground biomass, 95% of the total aboveground biomass, 6% of the total aboveground biomass, 94% of the total aboveground biomass, and 96% of the total aboveground biomass were mixed forest leaves and branches. Formula 3. Considering the secondary canopy penetration and multiple reflections, the latent heat flux of the model was increased from 0.769 to 0.792, RMSE from 50.77 W/m2 to 47.84 W W/m2, sensible heat flux from 0.684 to 0.705, RMSE from 48.42 W/m2 to 45.86 W/m2, and latent heat flux from simulated value to measured value. There is a significant correlation; the monthly mean diurnal variation of latent sensible heat is a single peak curve, reaching the maximum at noon, lower at night and early morning; compared with sensible heat flux, the seasonal variation of latent heat flux is more significant, in the growing season is much higher than the non-growing season, and has a positive correlation with vegetation growth, sensible heat flux. Before the data assimilation, RMSE was 0.1198, 0.5 h interval, RMSE was reduced to 0.0293, and the simulation results were significantly improved. The higher the frequency of assimilation data, the simulated soil moisture and the observed soil moisture after assimilation. The smaller the RMSE, the better the assimilation simulation effect. When the assimilation frequency was 15 days, the ability of the assimilation system to simulate soil moisture was almost the same as that of the non-assimilation model. The sensitivity S was 0.594, the sensitivity grade was IV; NPP remained basically unchanged with the increase of precipitation, indicating that the range of precipitation which could inhibit or promote vegetation growth was not included in the range of 30% to 30% precipitation, the sensitivity S was 0.0005, the sensitivity grade was I; NPP and solar radiation showed a nonlinear positive phase. The sensitivity S is 0.310, the sensitivity grade is III; the sensitivity S is 0.015 and the sensitivity grade is I with the increase of wind speed; the sensitivity S is 0.159 and the sensitivity grade is II.6, and the sensitivity S is 0.310 and 0.015 respectively; the sensitivity S is non-linear positive correlation with the relative humidity, the sensitivity S is 0.159 and the sensitivity grade is II.6. The normalized growth curve of LAI was extracted and the maximum value of LAI was estimated based on the traditional remote sensing statistical model. The time series LAI was estimated by the simple multiplication of the two methods. Compared with the measured values, the RMSE of broadleaf forest and mixed forest were 0.40 and 0.49, respectively, and 0.59 respectively. The main photosynthetic parameters optimized by BEPS Hourly model can be directly introduced into the BEPS Daily model to achieve a higher simulation capacity; the seasonal variation of GPP and NPP is more significant, showing a positive correlation with the growth of vegetation and the increase of temperature; at the regional scale, the optimized BEPS Daily model is used to carry on According to the regional NPP estimation, the average GPP and NPP per unit area in Maoershan area in 2011 were 1265.56 g C.m-2.a-1,628.40 g C.m-2.a-1, respectively. The primary productivity of different forest types was coniferous forest of broadleaf mixed forest.
【學(xué)位授予單位】:東北林業(yè)大學(xué)
【學(xué)位級(jí)別】:博士
【學(xué)位授予年份】:2016
【分類(lèi)號(hào)】:S718.5
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