GSMSR (GeoStatistical Model of Soil Respiration)

The GSMSR model, which is driven by monthly air temperature, monthly precipitation, and soil organic carbon (SOC) density, can capture 64% of the spatiotemporal variability of soil R(s).

region-scaleSoil Respirationspatiotemporal variability



Initial contribute: 2020-01-11


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Application-focused categoriesNatural-perspectiveLand regions

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Quoted fromYu, Guirui, Zemei Zheng, Qiufeng Wang, Yuling Fu, Jie Zhuang, Xiaomin Sun, and Yuesi Wang. "Spatiotemporal pattern of soil respiration of terrestrial ecosystems in China: the development of a geostatistical model and its simulation." Environmental science & technology 44, no. 16 (2010): 6074-6080. 

(1) Test of the Capability of a Global-Scale Rs Model

The Rs equations based on Lloyd and Talyor (17) include the van’t Hoff, Arrhenius, and Lloyd−Taylor equations. We compared the three equations, and found that no significant difference existed among the results, therefore, we used the van’t Hoff equation to quantify the dependence of Rs on temperature (eq 1) because the majority of our studied ecosystems, which were free of the stresses of water and other environmental factors, had a fixed temperature sensitivity.where Rs is the instantaneous soil respiration rate (μmol m−2 s−1), R0 is soil respiration rate at a reference temperature of 0 °C (μmol m−2 s−1), and t is actual air temperature (°C).
The following two equations were used to analyze the response of monthly Rs to temperature and precipitation, respectively (5),where Rs,monthly is monthly mean Rs rate (g C m−2 d−1), T is monthly air temperature, P is monthly precipitation, Q is temperature sensitivity of Rs, and P0 and K are regression parameters.
Raich et al. (5) presented TP model (eq 4) to evaluate the impacts of temperature and precipitation on the spatiotemporal pattern of the global Rs,where the definitions of variables and parameters are the same as those in above equations. The values of parameters are R0 = 1.250, Q = 0.055, and K = 4.25 (5).
In this study, the ability of TP model for estimating the spatiotemporal variability of Rs,monthly was tested using the above parameter values and the collected 333 monthly mean Rs data. The results show that the coefficient of determination between the simulated and measured Rs values was only 0.37 with considerable systematic error. Therefore, TP model was reparameterized using the collected data. The model modified with new parameter values (R0 = 1.740, Q = 0.029, K = 0.911) is referred to as TP2 model. However, TP2 model could only explain 40% of the spatiotemporal variability of the monthly mean Rs showing limited improvement in the systematic error (Figure S1).

(2) Construction of a New Rs Model

Neither the original TP model nor the reparameterized TP model (i.e., TP2 model) could well explain the spatial variability of Rs,monthly in China. When comparing the averages of the measured and predicted Rs,monthly of the same ecosystem, large systematic errors of TP and TP2 models still existed, partly due to their insufficient capture of the variability of Rs,monthly of different types of ecosystems at the same site.
Figure 2 illustrates the residual of simulated Rs,monthly. The strong correlation between the residual and SOC density suggests that SOC density was an additional factor controlling the spatial variability of Rs,monthly.

Figure 2. Relationship between SOC density at a depth of 20 cm and residual of simulated Rs,monthly: (a) TP; (b) TP2; ●: forest; ○: grassland; ▼: cropland. Residuals of simulated Rs,monthly values were averaged within each ecosystem.

Based on the above results, we propose a new Rs model (eq 5) by modifying the parameter R0 in eq 4 as a linear function of SOC density. The resulting model iswhere DS is SOC density at a soil depth of 20 cm, RDs=0 is the Rs,monthly rate when the SOC density is zero, and M is parameter. The TP model has the implicit assumption of “zero-precipitation−zero-respiration”, which is actually not the scenario of natural processes. In eq 5, taking an approach similar to that by Reichstein et al. (7), we added a parameter P0 to the model for taking into account the capability of water retention in soil.
Furthermore, Zheng et al. (12) found that the spatial variability of Q in China could be described by an exponential function of air temperaturewhere α and β are fitted parameters. By putting the eq 6 into eq 5, a new geostatistical model of soil respiration (GSMSR) can be obtained (eq 7) as follows
In this study, 333 Rs data selected randomly from a total of 390 collected data were used for parametrization of the GSMSR model. The resulting parameters were RDs=0 = 0.588, M = 0.118, α = 1.83, β = −0.006, P0 = 2.972, and K = 5.657.



Guirui Yu and GSMSR team (2020). GSMSR (GeoStatistical Model of Soil Respiration), Model Item, OpenGMS,


Initial contribute : 2020-01-11



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