The response of global soil respiration (Rs) to climate change determines how long the land can continue acting as a carbon sink in the future. This dissertation research identifies how temporal and spatial variation in environmental factors affects global scale Rs modeling and predictions of future Rs under global warming. Chapter 1 describes the recommend time range for measuring Rs across differing climates, biomes, and seasons and found that the best time for measuring the daily mean Rs is 10:00 am in almost all climates and biomes. Chapter 2 describes commonly used surrogates in Rs modeling and shows that air temperature and soil temperature are highly correlated and that they explain similar amounts of Rs variation; however, average monthly precipitation between 1961 and 2014, rather than monthly precipitation for a specific year, is a better predictor in global Rs modeling. Chapter 3 quantifies the uncertainty generated by four different assumptions of global Rs models. Results demonstrate that the time-scale of the data, among other sources, creates a substantial difference in global estimates, where the estimate of global annual Rs based on monthly Rs data (70.85 to 80.99 Pg C yr-1) is substantially lower than the current benchmark for land models (98 Pg C yr-1). Chapter 4 simulates future global Rs rates based on two temperature scenarios and demonstrates that temperature sensitivity of Rs will decline in warm climates where the level of global warming will reach 3°C by 2100 relative to current air temperature; however, these regional decelerations will be offset by large Rs accelerations in the boreal and polar regions. Chapter 5 compares CO2 fluxes from turfgrass and wooded areas of five parks in Blacksburg, VA and tests the ability of the Denitrification-Decomposition model to estimate soil temperature, moisture and CO2 flux across the seasons.
Cumulatively, this work provides new insights into the current and future spatial and temporal heterogeneity of Rs and its relationship with environmental factors, as well as key insights in upscaling methodology that will help to constrain global Rs estimates and predict how global Rs will respond to global warming in the future. / Ph. D. / CO₂ flux emitted from global soil is the second largest carbon exchange between the land and atmosphere. Accurately estimating global soil CO₂ flux and how it responds to climate change is critical to predict terrestrial carbon stocks. The objectives of this dissertation are to evaluate how time-scale affects our ability to estimate global soil CO₂ flux. In Chapter 1, we show that the best time period for measuring daily mean soil CO₂ flux is at around 10:00 am in almost all climate regions and vegetation types. The previously recommended time range (09:00 am and 12:00 pm) reasonably captures the daily mean soil CO₂ flux. The results from Chapter 2 indicate that air temperature is a good proxy for soil temperature in modeling global soil CO₂ flux. However, monthly precipitation is a uniformly poor proxy for soil water content; instead, average monthly precipitation is a better predictor for global soil CO₂ flux modeling. Chapter 3 demonstrates that the time-scale used in parameterizing models strongly affects the prediction of global CO₂ flux. When using monthly time-scale soil CO₂ flux and air temperature data, soil CO₂ flux increases as air temperature increases at air temperatures below 27 ℃, but soil CO₂ flux begins to decrease when air temperature is over 27 ℃. However, when using annual time-scale data, this response to temperature is masked, soil CO₂ flux increases as air temperature increases in all temperature conditions. As a result, the estimate of global annual soil CO₂ flux, based on monthly soil respiration data (70.85 to 80.99 Pg C yr⁻¹ ), is lower than the estimate based on the annual soil respiration data (98 Pg C yr⁻¹ ). Chapter 4 shows that if the level of global warming maintains its current rate (3ºC by the year 2100), then the annual soil CO₂ flux will either decrease or remains the same in arid, winter-dry temperate and tropical climate regions. However, these regional decelerations were offset by large soil CO₂ flux accelerations in the boreal and polar regions. Chapter 5 shows a significant difference in CO₂ flux among the five selected parks in Blacksburg, VA. The Denitrification-Decomposition model, despite having been developed for agriculture and undeveloped lands, closely estimates soil temperature, moisture and CO₂ flux across the seasons and therefore can be used to estimate and understand CO₂ fluxes from urban ecosystems in future studies.
This study highlights that the relationship between soil CO₂ fluxes and environmental factors such as air temperature and precipitation differs from region to region. The study also demonstrates that daily and monthly time-scale soil CO₂ fluxes and environmental data help constrain global soil CO₂ flux estimates and help to predict how global soil CO₂ fluxes will respond to global warming in the future.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/92195 |
| Date | 05 February 2018 |
| Creators | Jian, Jinshi |
| Contributors | Crop and Soil Environmental Sciences, Steele, Meredith K., Hodges, Steven C., Day, Susan D., Campbell, James B. Jr., Thomas, R. Quinn |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
Page generated in 0.3839 seconds