Understanding the effects of nitrogen (N) addition on dissolved carbon in boreal forest soils is essential for accurate evaluation of regional carbon balances. The objective of this study was to determine the effects of different levels and types of N addition on soil dissolved carbon concentration in a cold-temperate coniferous forest through an in-situ fertilization experiment. Simulated atmospheric N addition was applied in a factorial experiment with N addition level (control, 10, 20 and 40 kg of N ha −1 yr −1 ) and N type (NH 4 Cl, KNO 3 and NH 4 NO 3 ) treatments. The experiment was conducted over the 2010 growing season (May-September) at the Kailaqi farm of Genhe Forestry Bureau, located in the northern Great Xin’an mountain range, northern China. Monthly N addition treatments were applied in three replicate plots per treatment (n = 36), and measurements of dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) were derived from monthly sampling of the organic and mineral soil horizons. There was a significant effect of N type, with the combined N source (NH 4 NO 3 ) producing significantly higher DOC than the control (ambient addition) or the NH 4 Cl treatment in both the organic and mineral layers. The N addition treatment increased DIC in the organic layer at the low levels only, while N type did not have a significant effect. There was a significant interaction of the month and the N level treatment, as low level N addition tended to increase the content of soil DOC while high level N tended to inhibit soil DOC content, with these trends being most pronounced in the middle of the growing season. These results elucidate the importance of the type and timing of N additions to the dynamics of soil carbon pools.
For several decades, warming-induced fires have been widespread in many forest systems. A forest fire could be a potential indicator, since the Great Xing’an Range is susceptible to global climate changes and frequent extreme events. This region has a relatively integrated forest community structure. This paper investigated 35 factors to explore how natural conditions affect fire scale. We analyzed the fire spatiotemporal distribution, by combining the Google Earth Engine (GEE) platform and historical records, and then reconstructed the fire-prone climate conditions. We used an exploratory model to minimize the climate factors and a geographically and temporally weighted regression (GTWR) model to predict regional large-scale lightning fire occurrence. The main results are (1) Lightning fire occurrence increased during the past four decades, and the regional fire season starts from the spring (May to June). (2) The time of occurrence of lightning fires had a strong correlation with the occurrence density. (3) The main natural factors affecting a fire-affected area are air moisture content, topographic slope, maximum surface air temperature, wind direction, and surface atmospheric pressure. The regional climate can be characterized that the prevailing southeastern wind bringing lots of precipitation and strong surface pressure, combined with the regional periodic lightning weather and irregular high temperatures, forming fire-prone weather. The abnormal soil water content in the spring led to vegetation growth and increased fuel storage. The low air water content and long-term water deficit made the local air dry. Lightning strikes are an influential factor in fire frequency, while climatic conditions shape the fire-affected areas. (4) The seven days of pre-fire data are more accurate for studying lightning fire occurrence. The GTWR model showed the best fitness among the four models. Fire-prone areas showed a trend of increasing from south to north. In the future, lightning fires will likely occur in this region’s north and east. Our work would promote the local forest fire policy-making process.
Forest fires lead to permafrost degradation and localized drought, and regional droughts increase the probability of forest fires, leading to a positive feedback loop between climate change and fires. However, the relationship between fire occurrence and climatic factors change is unclear for boreal forests, which represent the largest land-based biome and stock of carbon. Here, we analyzed the relationship between lightning fire occurrence and meteorological and topographic factors based on the fire frequency, burned area, and meteorological data from the primeval forest region of the northern Daxing’an Mountains in China. We found that lightning fires occurred most frequently at an altitude of 600 to 700 m. From 1999 to 2019, the frequency of lightning fires showed an overall upward trend, whereas the affected area had no obvious change. It can be attributed to fire suppression efforts and greatly increased investment in fire prevention in China. Snow cover had a strong regulatory effect on the start and end dates of lightning fires for seasonal cycle. The frequency of lightning fires was positively correlated with the average temperature, maximum temperature, and surface evaporation and negatively correlated with precipitation and surface soil moisture (0–10 cm). The result will be useful in the spatially assessment of fire risk, the planning and coordination of regional efforts to identify areas at greatest risk, and in designing long-term lightning fires management strategies.
More and more droughts happened during the last decades, threatening natural forests in the semi-arid regions of North China. The increase in drought pressure may have an impact on stand transpiration (T) in semi-arid regions due to rising temperature and changes in precipitation. It is unclear how the transpiration of natural forest in semi-arid regions respond to drought, which is regulated by environmental factors. In this study, a relatively simple but mechanism-based forest stand T model that couples the effects of the reference T, solar radiation (Rn), vapor pressure deficit (VPD), and relative extractable water (REW) in the 0–80 cm soil layer was developed to quantify the independent impacts of Rn, VPD, and REW on T. The model was established based on the observed sap flow of four sample trees, and environmental factors were observed from May to September in different hydrological years (2015, 2017, 2018, and 2021) in a pure white birch (Betula platyphylla Sukaczev) forest stand in the southern section of the Greater Khingan Mountains, northeastern China. The sap flow data were used to calculate tree transpiration (Tt) and T to calibrate the T model. The results indicated that (1) The Tt sharply declined in the ‘dry’ year compared with that in the ‘wetter’ year. The daily Tt for small trees in the ‘dry’ year was only one-fifth of that in the ‘wetter’ year, and the daily Tt of large trees was 48% lower than that in the ‘normal’ year; (2) Large trees transpired more water than small trees, e.g., the daily Tt of small trees was 89% lower than that of the large trees in the ‘normal’ year; (3) Daily T increased with the increase in Rn, and the response conformed to a binomial function. Daily T responded to the rise of VPD and REW in an exponential function, first increasing rapidly, gradually reaching the threshold or peak value, and then stabilizing; (4) The driving factors for the T shift in different hydrological years were the REW in the ‘dry’ year, but the Rn and REW in the ‘wet’, ‘normal’, and ‘wetter’ years. The REW in the ‘wet’ and ‘wetter’ years exerted positive effects on T, but in the ‘normal’ and ‘dry’ year, exerted negative effects on T. Thus, the environmental factors affecting T were not the same in different hydrological years.
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