Research Of Carbon Sink Capacity Caused By Rock Weathering Process In China

Rock weathering caused by the carbonic acid reaction with minerals to produce dissolved bicarbonates which are carried by rivers to the oceans is an important part in carbon cycle. The process causes significant carbon dioxide consumption, the accurate calculation of which may partly explains the missing sink of carbon and may do some contribution to the research in Global Change.The CO2 uptake by carbonate dissolution (which is also called karstification) on the continents is counterbalanced by the CO2 release by carbonate precipitation in the ocean. The same is not true for silicate weathering. Silicate weathering is more important than carbonate weathering as a long-term control on atmospheric CO2.Rock weathering is controlled by a great range of factors which includes rock property, climatic conditions, CO2 concentration in atmosphere/soil, vegetation and so on. Analyses of the influences of factors to rock weathering show that sometimes the weathering process may be controlled mainly by one factor, but in most cases rock weathering is controlled by all factors integratedly.A global erosion model (GEM-CO2) developed by Amiotte Suchet allows to calculate the flux of atmospheric/soil CO2 consumed by chemical erosion of continental rocks. In the thesis, the CO2 consumption by rock weathering in China is estimated based on GEM-CO2 and the Chinese Resources and Environment Database, and its distribution is show in a GRID map with a spatial resolution of 1000m*1000m. The total carbon consumption is about 4.72*107TC/a, about 52.65% of which are caused by carbonate and 47.35% of which are caused by silicate. The model results are close to previous estimation by other researches. The flux of CO2 consumed by rock weathering increases where carbonate rock outcrops are more abundant and when drainage intensity increases. The results show that the main consumption of CO2 is localized in Guangxi province, Guizhou province, Chongqin, North Hubei province, and Southwest of Hunan province, because of large area with a high proportion of carbonate rocks and high humidity.Karstification is a good connector of Geological processes and Biological processes. Many methods are tried in order to accurately estimate the karst solutional speed and then the CO2 consumption by karstification, such as analyzing elements in river, dissolution kinetics, dissolution survey experiments and so on. In the thesis, the karst solutional speed is estimated by a model. Analyses of the influences of some factors to karstification get following conclusions: 1 .Temperature and precipitaion are main climatic factors involving in karstification. There is an obvious positive correlation between the karst solutional speed and precipitation. But it's not the same between the karst solutional speed and temperature. Temperature influences karstification in a more complex way; 2.Biological processes influences karstification through increasing the acidity of water by metabolic outcome, and then accelerating the reaction between water and rock and hastening the releasing of Calcium element from soil. The intensity of biological process can be assessed with the value of NPP(Net Primary Productivity); 3.As part of reactor, the soil CO2 drives karstification, which can be verified by the positive correlation between the concentration of [HCO3 ] in river or in spring and the CO2 concentration in soil. There is a positive correlation between the intensity of soil respiration and the CO2 concentration in soil.Based on above knowledge and data from continuous experiments and observations, a karst solutional speed model in tropic and subtropical zone in China is developed to describe the relationship between karst solutional speed and temperature, precipitation, NPP, and intensity of soil respiration. The data was non-dimensionized to improve the comparability of different parameters. The archetype of the model was selected with Response Surface Analysis. The effect of correlations between independent factors was analyzed. And the regression equation was worked out...