Effects of Temperature and Moisture on Soil Organic Matter Decomposition Along Elevation Gradients on the Changbai Mountains, Northeast China

Decomposition of soil organic matter(SOM) is of importance for CO_2 exchange between soil and atmosphere and soil temperature and moisture are considered as two important factors controlling SOM decomposition. In this study, soil samples were collected at 5 elevations ranging from 753 to 2 357 m on the Changbai Mountains in Northeast China, and incubated under different temperatures(5, 10, 15, 20, 25, and 30?C) and soil moisture levels(30%, 60%, and 90% of saturated soil moisture) to investigate the effects of both on SOM decomposition and its temperature sensitivity at different elevations. The results showed that incubation temperature(F = 1 425.10, P < 0.001), soil moisture(F = 1 327.65, P < 0.001), and elevation(F = 1 937.54, P < 0.001) all had significant influences on the decomposition rate of SOM. The significant effect of the interaction of incubation temperature and soil moisture on the SOM decomposition rate was observed at all the 5 sampling elevations(P < 0.001). A two-factor model that used temperature and moisture as variables fitted the SOM decomposition rate well(P < 0.001) and could explain 80%–93% of the variation of SOM decomposition rate at the 5 elevations. Temperature sensitivity of SOM decomposition, expressed as the change of SOM decomposition rate in response to a 10?C increase in temperature(Q_(10)), was significantly different among the different elevations(P < 0.01), but no apparent trend with elevation was discernible. In addition, soil moisture and incubation temperature both had great impacts on the Q_(10) value(P < 0.01), which increased significantly with increasing soil moisture or incubation temperature. Furthermore, the SOM decomposition rate was significantly related to soil total Gram-positive bacteria(R^2= 0.33, P < 0.01) and total Gram-negative bacteria(R^2= 0.58, P < 0.001). These findings highlight the importance of soil moisture to SOM decomposition and its Q_(10) value,which needs to be emphasized under warming climate scenarios.

Investigating low-permeability sandstone based on physical experiments and predictive modeling

An innovative method is proposed for preparing low-permeability sandstone with different moisture saturation.The permeability of the prepared low-permeability sandstone sample is measured under different confinement and seepage pressures.Based on the experimental results,10 types of different machine-learning models combined with optimization algorithms are established to predict the permeability of low-permeability sandstone.A comprehensive evaluation and comparison of the 10 types of machine-learning models are conducted to identify the machine-learning model with the best performance.Next,a sensitivity analysis is conducted on the factors influencing the permeability of low-permeability sandstone to elucidate the internal mechanism according to the established machinelearning model.The following conclusions are drawn.With an increase in the confinement pressure,the permeability of lowpermeability sandstone with different moisture-saturation levels decreases,and the permeability of low-permeability sandstone decreases with an increase in the moisture saturation.The hybrid particle swarm optimization algorithm-backpropagation artificial neural network(PSO-BPANN)model provides the best results for predicting the permeability of low-permeability sandstone.The established PSOBPANN model is also reliable for predicting the permeability of low-permeability sandstone from other engineering sites.Among the influencing factors,moisture saturation has the largest effect on the permeability of low-permeability sandstone,followed by the confinement pressure.