RESEARCH/GRANTS INVOLVED (in total of ~$50 Million)

1.      The World’s First Drought and Insect Caused Global Tree Mortality Monitoring System,                    Feb 2013-Feb 2015

Funding: LANL Early Career Award, 450,000$;    PI: Chonggang Xu


The critical urgency of forecasting climate impacts and feedbacks makes understanding, quantifying, and predicting terrestrial carbon balance and subsequent climate impacts one of the greatest science challenges currently facing the world. The real-time monitoring systems for dominant types of disturbances will provide a key foundation for our understanding of global carbon balance.  We currently already have a fire-burned area monitoring system and a comprehensive land-use change database; however, there is no global monitoring system of drought/insect-caused vegetation mortality, which could be at similar magnitude of fire-caused tree mortality. Armed with the world’s leading capability in dynamic vegetation modeling and tree mortality research, we propose to develop the world’s first automated global drought/insect-caused tree mortality monitoring system. The mortality monitoring system is developed based on the fusion of different sources of information including real-time mortality signal from remote sensing imagery, vegetation change information simulated from a vegetation dynamics model, radiative transfer and reflectance information from a forest reflectance model, and different sources of background information from forest inventory and remote sensing products. The fusion of different sources of information makes it feasible for the first time in the world to accurately quantify tree mortality using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery from NASA, which is a common remote sensing tool for monitoring earth system processes globally. This detailed tree mortality quantification has never been possible by analyzing MODIS image alone. The successful development of our monitoring system will represent an enormous leap forward in our understanding of terrestrial carbon feedback to atmosphere, which is a key area of climatic change research in LANL’s mission to understand and predict the impacts of global energy demand


Role: PI


2.      Next Generation Carbon-Nitrogen Model                      July 2012- July 2015


Funding: UC Laboratory Fee, $ 1 million; PI: Chonggang Xu; Co-PI: Jasper Vrugt


The impact of energy use on climate depends in large part on the response of terrestrial ecosystems that regulate atmospheric CO2 and climate through exchange and storage of carbon and energy. Nitrogen is a dominant regulator of vegetation dynamics and the terrestrial carbon cycle, yet rather simplistic empirical-type models are still used to predict the effect of nitrogen limitation and light competition on vegetation growth. Therefore, a large uncertainty exists in the current simulation of nitrogen related processes (e.g., photosynthesis and soil carbon storage response to nitrogen addition), which substantially affects the reliability of predicted terrestrial carbon fluxes. To reliably assess energy impacts on the global carbon cycle and future climates, we propose to develop, test, and calibrate a next generation carbon-nitrogen dynamics model and integrate this model into the Community Earth System Model (CESM) developed mainly by the National Center of Atmospheric Research (NCAR), Los Alamos National Laboratory (LANL) and many other universities. Our dynamic carbon-nitrogen model will incorporate recent advances in nitrogen modeling and use recent advances in Markov Chain Monte Carlo simulation to rigorously calibrate and evaluate the developed model against observations, including soil fertilization and free air CO2 enrichment (FACE) observations across a range of different forest types. The calibrated model will be used to assess the effects of different energy use scenarios on global climates.


Role: PI


3.      Next Generation Ecosystem Experiments in the Arctic                        Oct 2011 -2014


Funding: DOE office of science, $24million (8 million per year for 3 years for Phase I and potential extension with another 7 years of Phase); PI: Stan Wullschleger (ORNL)


This is a joint project among 3 major national labs: Los Alamos National Lab, Oak Ridge National Lab and Lawrence Berkeley National Laboratory. The target of the project is to conduct large-scale warming experiment in the arctic for a better understanding of subsurface, geophysics, ecosystem and landscape dynamics. The NCAR CLM will be used to direct the experimental research and integrate new insights from the experiments.  I am involved in vegetation dynamic modeling and synthesis.


Role: Co-investigator and the lead modeler in the dynamic vegetation theme


4.      Terrestrial Vegetation, CO2 Emissions, and Climate Dynamics; Oct 2010-present


Funding: LANL, $ 5 million, PI: Nathan McDowell


I am responsible for data assimilation and uncertainty analysis by integrating the ARCHY-ED model with remote sensing (QUICK Bird and MODIS data), forest inventory, and eddy flux tower data to better understand and predict drought-related mortality and the resulting effects on global carbon cycle.


  Role: Co-I, Lead of vegetation modeling and uncertainty analysis


5.      Predicting Climate Impacts and Feedbacks in the Terrestrial Arctic    Oct 2011 –Current


Funding: LANL, $5 million, PI: Scott Painter


Develop an advanced Arctic Terrestrial Simulator (ATS) for modeling the complex interactions among thermal, mechanical, biogeochemical, ecological and hydrologic permafrost processes. I am responsible for biogeochemical cycle modeling.


Role: Co-Investigator


6.      Quantification and reduction of critical uncertainties associated with carbon cycle – climate system feedbacks, Oct 2010-May 2013


Funding: DOE Office of Science, $3 million, PI: Peter Thornton, Co-PI: Nathan McDowell


Our objectives in this project are: 1) to quantify critical uncertainties in global-scale climate predictions associated with carbon-climate feedbacks; 2) to improve our understanding and model representation of processes controlling these feedbacks through zonally-specific model-data evaluation exercises; and 3) to extend our data-based evaluation to quantification of carbon-climate feedback responses and uncertainties in the large population of global scale carbon-climate models contributing to the Fifth Climate Model Intercomparison Project (CMIP5). I am responsible to improve the nitrogen effect on soil respirations in the NCAR CLM model by integrating that with observational data.


Role: Co-Investigator


7.      Regional Climate Modeling,                                              May 2010-Sep 2011

Funding: DOE $ 6 million, PI: Cathy Wilson, Co-PI: Nathan McDowell


I am responsible for coupling a 3-D soil hydrological model (ARCHY) and a mechanistic vegetation dynamic model (ED) to understand the interactions between permafrost thawing and vegetation growth under the context of global climatic change.


Role: Post-doctoral Fellow



8.      Improving Robustness of a Tactical Model of Aedes/Dengue Dynamics 2011-2015


Funding: NIH ($346,867, 1R01AI091980-01,PI: Fred Gould, North Carolina State University)         

I am responsible for model-data integration including parameter estimation, uncertainty analysis and model improvements with new field and experimental data collected from sites from New Mexico, Australia and Peru.


Role: Collaborator, actively involved in the proposal writing with my uncertainty work making a great contribution to the proposal.


9.      Population Genetics of Transgenes in Mosquito Vectors          May 2009-May 2010


Funding: NIH (R01-AI54954-0IA2, $750,000, PI: Fred Gould, North Carolina State University)                         

The goal of my task is to quantify uncertainties in the equilibrium population dynamics predicted from a spatial model of mosquito population (Skeeter-Buster) in its application to the Iquitos city in Peru. Uncertainties in the model predictions come from two major sources: 1) uncertainties in the estimation of 67 parameters accounting for mosquito survival, development, fecundity, environmental thresholds, and spatial dispersal; and 2) uncertainty due to simulated environmental and demographic stochasticity.


       Role: Post-doctoral Fellow


10.  Forest Landscape Dynamics Under ClimateChange                                    2004-2009


Funding: USDA McIntire-Stennis funds (MS 875-359, PI: George Gertner, University of Illinois at Urbana-Champaign)


The goal of my task is to examine the potential forest landscape response to climatic change based on a hierarchical response of ecosystem at different levels, including species physiology (e.g. net primary production) and seedling establishment change at the species level, the colonization and competition processes modification at the forest succession level, the species composition change at the community level and finally the landscape pattern change at the landscape level. For this purpose, I have used different statistical methods to examine how lower-scale processes can affect the higher-scale processes and patterns through a coupled modeling system by a forest landscape model (LANDIS-II) and a forest ecosystem process model (PnET-II: include water, carbon and nutrient cycle processes).


Role: Research Assistant


11.  Uncertainty and Sensitivity Analysis Methodology Development               2004-2009


Funding: U.S. Army Corps of Engineers Construction Engineering Research Laboratory (CERL W9132T-06-2-0001, PI: George Gertner, University of Illinois at Urbana-Champaign)


Uncertainty and sensitivity analysis is a statistical method to assess how much uncertainty there is in the model prediction and where the uncertainty comes from. Uncertainty and sensitivity analysis can help scientists target at processes/parameters that make large contributions to ecological/environmental system prediction, which can be very useful for natural resources conservation, management, and general understanding of ecological processes. The previous methods for model uncertainty and sensitivity analysis are commonly based on the assumptions of parameter independence. However, for most of the realistic model applications, the parameters are correlated. The goal of my task is to develop uncertainty and sensitivity methods for models with correlated parameters.


Role: Research Assistant


12.  Forest Landscape Modeling in Northeastern China                                2000-2004


Funding: Chinese Academy of Sciences ($300, 000, PIs: Hong S He and Yuanman Hu)


This project is targeted to understand the forest landscape dynamics in both natural and managed forests using a forest landscape model of forest succession, disturbances and management (LANDIS 3.7). I was involved in model parameterization (designed a stochastic algorithm to assign forest stand-based age and species information to individual cell/sites and remote sensing classification), uncertainty and sensitivity analysis, and assessing the forest landscape change under harvesting and fire disturbances. 


Role: Research Assistant


13.  Soil Erosion and Non-point Pollution                                                                             1999-2000


Funding: Chinese Academy of Sciences (PI: Ning Wang, Northeast Normal University, Changchun, China)


This project is targeted to assess the nitrogen loss as a result of soil erosion in the Songhua watershed, Jilin Province, China. I used ARC/INFO to digitize the topographic maps, based on which I estimated elevations and slopes. After combining the vegetation map, soil type map and precipitation data, I applied the Universal Soil Loss Equation to estimate the soil loss and the corresponding nitrogen release into the Songhua River.


      Role: Undergraduate Research Assistant