The permafrost carbon inventory on the Tibetan Plateau : a new evaluation using deep sediment cores

Acknowledgements We are grateful for Dr. Jens Strauss and the other two anonymous reviewers for their insightful comments on an earlier version of this MS, and appreciate members of the IBCAS Sampling Campaign Teams for their assistance in field investigation. This work was supported by the National Basic Research Program of China on Global Change (2014CB954001 and 2015CB954201), National Natural Science Foundation of China (31322011 and 41371213), and the Thousand Young Talents Program.Peer reviewedPostprin

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

Sources of Atmospheric Fine Particles and Adsorbed Polycyclic Aromatic Hydrocarbons in Syracuse, New York

Land surface temperature (LST) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been widely utilized across scientific disciplines for a variety of purposes. The goal of this dissertation was to utilize MODIS LST for three spatial modeling applications within the conterminous United States (CONUS). These topics broadly encompassed agriculture and human health. The first manuscript compared the performance of all methods previously used to interpolate missing values in 8-day MODIS LST images. At low cloud cover (\u3c30%), the Spline spatial method outperformed all of the temporal and spatiotemporal methods by a wide margin, with median absolute errors (MAEs) ranging from 0.2°C-0.6°C. However, the Weiss spatiotemporal method generally performed best at greater cloud cover, with MAEs ranging from 0.3°C-1.2°C. Considering the distribution of cloud contamination and difficulty of implementing Weiss, using Spline under all conditions for simplicity would be sufficient. The second manuscript compared the corn yield predictive capability across the US Corn Belt of a novel killing degree day metric (LST KDD), computed with daily MODIS LST, and a traditional air temperature-based metric (Tair KDD). LST KDD was capable of predicting annual corn yield with considerably less error than Tair KDD (R2 /RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). The superior performance can be attributed to LST’s ability to better reflect evaporative cooling and water stress. Moreover, these findings suggest that long-term yield projections based on Tair and precipitation alone will contain error, especially for years of extreme drought. Finally, the third manuscript assessed the extent to which daily maximum heat index (HI) across the CONUS can be estimated by MODIS multispectral imagery in conjunction with land cover, topographic, and locational factors. The derived model was capable of estimating HI in 2012 with an acceptable level of error (R 2 = 0.83, RMSE = 4.4°F). LST and water vapor (WV) were, by far, the most important variables for estimation. Expanding this analytical framework to a more extensive study area (both temporally and spatially) would further validate these findings. Moreover, identifying an appropriate interpolation and downscaling approach for daily MODIS imagery would substantially increase the utility of the corn yield and HI models

Advancing the understanding for hydro-climatic controls on water balance and lake-level variability in the Tibetan Plateau: Hydrological modeling in data-scarce lake basins integrating multi-source data

The contrasting patterns of lake-level changes across the Tibetan Plateau (TP) are indicators of differences in the water balance over the TP. However, little is known about the key hydrological factors controlling this variability. The purpose of this study was to contribute to a more quantitative understanding of these factors for four selected lakes in the southern-central part of the TP: Nam Co and Tangra Yumco (increasing water levels), and Mapam Yumco and Paiku Co (stable or slightly decreasing water levels). Therefore, an integrated approach combining hydrological modeling, atmospheric-model output and remote-sensing data was developed. The J2000g hydrological model was adapted and extended according to the specific characteristics of closed-lake basins on the TP and driven with High Asia Refined analysis (HAR) data at 10 km resolution for the period 2001–2010. Differences in the mean annual water balances among the four basins are primarily related to higher precipitation totals and attributed runoff generation in the Nam Co and Tangra Yumco basins. Precipitation and associated runoff are the main driving forces for inter-annual lake variations. The glacier-meltwater contribution to the total basin runoff volume (between 14 and 30% averaged over the 10-year period) plays a less important role compared to runoff generation from rainfall and snowmelt in non-glacierized land areas. These results highlight the benefits of linking hydrological modeling with atmospheric-model output and satellite-derived data in regions where observation data are scarce, and the developed approach can be readily transferred to other data-scarce closed-lake basins, opening new directions of research