Geology topics

W. Yuan

Publications and source records attributed to W. Yuan.

5 recordsLinked to original sources

USGS2011

Redefinition and global estimation of basal ecosystem respiration rate

Basal ecosystem respiration rate (BR), the ecosystem respiration rate at a given temperature, is a common and important parameter in empirical models for quantifying ecosystem respiration (ER) globally. Numerous studies have indicated that BR varies in space. However, many empirical ER models still use a global constant BR largely due to the lack of a functional description for BR. In this study, we redefined BR to be ecosystem respiration rate at the mean annual temperature. To test the validity of this concept, we conducted a synthesis analysis using 276 site-years of eddy covariance data, from 79 research sites located at latitudes ranging from ∼3°S to ∼70°N. Results showed that mean annual ER rate closely matches ER rate at mean annual temperature. Incorporation of site-specific BR into global ER model substantially improved simulated ER compared to an invariant BR at all sites. These results confirm that ER at the mean annual temperature can be considered as BR in empirical models. A strong correlation was found between the mean annual ER and mean annual gross primary production (GPP). Consequently, GPP, which is typically more accurately modeled, can be used to estimate BR. A light use efficiency GPP model (i.e., EC-LUE) was applied to estimate global GPP, BR and ER with input data from MERRA (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate resolution Imaging Spectroradiometer). The global ER was 103 Pg C yr −1 , with the highest respiration rate over tropical forests and the lowest value in dry and high-latitude areas.

Global Biogeochemical Cycles↗

USGS2011

Are soils of Iowa USA currently a carbon sink or source? Simulated changes in SOC stock from 1972 to 2007

Upscaling the spatial and temporal changes in carbon (C) stocks and fluxes from sites to regions is a critical and challenging step toward improving our understanding of the dynamics of C sources and sinks over large areas. This study simulated soil organic C (SOC) dynamics within 0–100 cm depth of soils across the state of Iowa in the USA from 1972 to 2007 using the General Ensemble biogeochemical Modeling System (GEMS). The model outputs with variation coefficient were analyzed and assembled from simulation unit to the state scale based upon major land use types at annual step. Results from this study indicate that soils (within a depth of 0–100 cm) in Iowa had been a SOC source at a rate of 190 ± 380 kg C ha −1 yr −1 . This was likely caused by the installation of a massive drainage system which led to the release of SOC from deep soil layers previously protected under poor drainage conditions. The annual crop rotation was another major force driving SOC variation and resulted in spatial variability of annual budgets in all croplands. Annual rate of change of SOC stocks in all land types depended significantly on the baseline SOC levels; soils with higher SOC levels tended to be C sources, and those with lower levels tended to be C sinks. Management practices (e.g., conservation tillage and residue management practices) slowed down the C emissions from Iowa soils, but could not reverse the general trend of net SOC loss in view of the entire state due mainly to a high level of baseline SOC stocks.

Agriculture, Ecosystems and Environment↗

USGS2010

Impacts of precipitation seasonality and ecosystem types on evapotranspiration in the Yukon River Basin, Alaska

Evapotranspiration (ET) is the largest component of water loss from terrestrial ecosystems; however, large uncertainties exist when estimating the temporal and spatial variations of ET because of concurrent shifts in the magnitude and seasonal distribution of precipitation as well as differences in the response of ecosystem ET to environmental variabilities. In this study, we examined the impacts of precipitation seasonality and ecosystem types on ET quantified by eddy covariance towers from 2002 to 2004 in three ecosystems (grassland, deciduous broadleaf forest, and evergreen needleleaf forest) in the Yukon River Basin, Alaska. The annual precipitation changed greatly in both magnitude and seasonal distribution through the three investigated years. Observations and model results showed that ET was more sensitive to precipitation scarcity in the early growing season than in the late growing season, which was the direct result of different responses of ET components to precipitation in different seasons. The results demonstrated the importance of seasonal variations of precipitation in regulating annual ET and overshadowing the function of annual precipitation. Comparison of ET among ecosystems over the growing season indicated that ET was largest in deciduous broadleaf, intermediate in evergreen needleleaf, and lowest in the grassland ecosystem. These ecosystem differences in ET were related to differences in successional stages and physiological responses.

Water Resources Research↗

USGS2010

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively. Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000–2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.

Remote Sensing of Environment↗

USGS2007

Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes

The quantitative simulation of gross primary production (GPP) at various spatial and temporal scales has been a major challenge in quantifying the global carbon cycle. We developed a light use efficiency (LUE) daily GPP model from eddy covariance (EC) measurements. The model, called EC-LUE, is driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux (used to calculate moisture stress). The EC-LUE model relies on two assumptions: First, that the fraction of absorbed PAR (fPAR) is a linear function of NDVI; Second, that the realized light use efficiency, calculated from a biome-independent invariant potential LUE, is controlled by air temperature or soil moisture, whichever is most limiting. The EC-LUE model was calibrated and validated using 24,349 daily GPP estimates derived from 28 eddy covariance flux towers from the AmeriFlux and EuroFlux networks, covering a variety of forests, grasslands and savannas. The model explained 85% and 77% of the observed variations of daily GPP for all the calibration and validation sites, respectively. A comparison with GPP calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) indicated that the EC-LUE model predicted GPP that better matched tower data across these sites. The realized LUE was predominantly controlled by moisture conditions throughout the growing season, and controlled by temperature only at the beginning and end of the growing season. The EC-LUE model is an alternative approach that makes it possible to map daily GPP over large areas because (1) the potential LUE is invariant across various land cover types and (2) all driving forces of the model can be derived from remote sensing data or existing climate observation networks.

Agricultural and Forest Meteorology↗