presentation_2011.bib
@conference{Hoffman2011,
title = {Determining Shifts in Climate Regimes Using Earth System Model Projections},
author = {Forrest M. Hoffman and William W. Hargrove and Richard T. Mills and Jitendra Kumar and Salil Mahajan},
booktitle = {US Internatinal Association of Landscape Ecology (US-IALE) Annual Symposium, Portland, Oregon, April 3-7},
year = {2011},
month = {April},
file = {:Publications/Hoffman_USIALE2011.pdf:PDF},
owner = {jkumar},
timestamp = {2010.11.30}
}
@presentations{Hoffman2011b,
title = {Data Mining for Climate Change Model Intercomparison},
author = {Forrest M. Hoffman and Jitendra Kumar and William W. Hargrove},
howpublished = {First Workshop on Understanding Climate Change through Data (August 15--16, 2011), University of Minnesota, Minneapolis, Minnesota, USA (Invited)},
month = {August 16},
year = {2011},
owner = {jkumar},
timestamp = {2011.12.12},
url = {http://www.climatemodeling.org/~forrest/presentations/Hoffman_Understanding-Climate-Change-through-Data_20110816/}
}
@presentations{Kumar2011c,
title = {Data Mining in Earth System Sciences},
author = {Jitendra Kumar and Forrest M. Hoffman and Richard T. Mills and William W. Hargrove},
howpublished = {Oak Ridge Climate Change Science Institute (CCSI) Lunchtime Seminar, Oak Ridge, Tennessee, USA},
month = {August},
year = {2011},
owner = {jkumar},
timestamp = {2011.12.12},
url = {http://www.climatemodeling.org/~forrest/presentations/Kumar_CCSI-3x5_20110830.pdf}
}
@presentations{Kumar2011d,
title = {Data mining approaches for the analysis of large earth science data sets},
author = {Jitendra Kumar and Forrest M. Hoffman and Richard T. Mills and William W. Hargrove},
howpublished = {Workshop on Scientific Data and Analytics for Extreme Scale Computing, Tahoe City, Center for Scalable Application Development Software (CScADS), Rice University (Invited)},
month = {July},
year = {2011},
owner = {jkumar},
timestamp = {2011.12.12}
}
@conference{Kumar2011b,
title = {Effect of spatially and temporally variable recharge on subsurface reactive transport of contaminants at {O}ak {R}idge {I}ntegrated {F}ield {R}esearch {C}hallenge site},
author = {Jitendra Kumar and Peter C. Lichtner and Richard T. Mills and Glenn E. Hammond and Daniil Svyatskiy and Guoping Tang and Scott Brooks and David Watson and Jack Parker},
booktitle = {American Geophysical Union (AGU) Fall Meeting 2011, San Francisco, CA, December 5-9},
year = {2011},
month = {December},
abstract = {Recharge is one of the most fundamental components of groundwater systems which drives both flow and transport in the subsurface and plays an important role in the migration of contaminants at the Oak Ridge Integrated Field Research Challenge (ORIFRC) site. The area receives an average of 137 cm of precipitation per year, most of it during winter. About 50% of the precipitation is lost to evapotranspiration, 40% runs off directly to surface water, and less than 10% recharges to ground water. The migration of the reactive contaminant plume at the site is modeled using the massively parallel flow and reactive transport model PFLOTRAN. The geology at the site consists of dipping beds of limestone, shale and sandstone with strike N 55° E and dip 45° SE, over which is superimposed a highly porous, horizontally oriented, saprolite weathering profile. To model this system in 3-D a grid was constructed with x-axis aligned with the strike of the geologic formation and z-axis vertical. This formulation requires a full permeability tensor with off-diagonal components obtained by rotation of the principal axes tensor through the formation dip angle. A full tensor capability was implemented in PFLOTRAN using the mimetic finite difference (MFD) method, a mass conserving, second-order accurate scheme with auxiliary pressure degrees of freedom at grid cell faces. A complex geochemical fluid with 17 primary reactive species and a number of minerals was implemented to model the contaminant discharged from the S-3 ponds at the ORIFRC site. A 50-year history of observed rainfall at the site was used as input to the model to estimate transient recharge conditions and to study the effect of spatially and temporally varied recharge. Results from the investigations of impact of spatio-temporal variation in recharge on the migration of contaminant plume will be presented.},
file = {Kumar2011b.pdf:Publications/Kumar2011b.pdf:PDF},
owner = {jkumar},
timestamp = {2011.09.11}
}
@conference{Kumar2011a,
title = {Parallel Parameter Estimation for Subsurface Flow and Reactive Transport Problems},
author = {Jitendra Kumar and Richard T. Mills},
booktitle = {The Institute for Operations Research and the Management Sciences (INFORMS) Annual Meeting 2011, Charlotte, NC, November 13-16},
year = {2011},
month = {November},
abstract = {Aquifers are comprised of stratigraphic units with highly heterogeneous material properties. Inferring these properties from spatially and temporally sparse field observations is a complex non-linear and high dimensional problem. We have applied a massively parallel evolutionary algorithm to this task for simulations of sites on the Oak Ridge reservation using the PFLOTRAN parallel flow and reactive transport model.},
owner = {jkumar},
timestamp = {2011.09.11}
}
@presentations{Lichtner2011,
title = {Assessment of Coupled Plume-Scale Processes at the {O}ak {R}idge {IFRC} using High Performance Computing},
author = {Lichtner, P. C. and Hammond, G. E. and Kumar, J. and Mills, R. T. and Parker, J. and Svyatskiy, D. and Tang, G. and Watson, D. and Brooks, S. C.},
howpublished = {DOE-SBR 6th Annual PI Meeting, Washington, D.C., USA, April 26-28},
month = {April},
year = {2011},
owner = {jkumar},
timestamp = {2011.06.20}
}
@presentations{Lichtner2011a,
title = {{PFLOTRAN}: The Next-Generation Peta-scale Subsurface Reactive Flow and Transport Code},
author = {Lichtner, P. C. and Hammond, G. E. and Mills, R. T. and Philip, B. and Svyatskiy, D. and Bisht, G. and Kumar, J. and Lu, C. and Chen, X. and Smith, B.},
howpublished = {DOE-SBR 6th Annual PI Meeting, Washington, D.C., USA, April 26-28},
month = {April},
year = {2011},
owner = {jkumar},
timestamp = {2011.06.20}
}
@presentations{Lichtner2011b,
title = {{PFLOTRAN}: The Next-Generation Peta-scale Subsurface Reactive Flow and Transport Code},
author = {Lichtner, P. C. and Hammond, G. E. and Mills, R. T. and Philip, B. and Svyatskiy, D. and Bisht, G. and Kumar, J. and Lu, C. and Chen, X. and Smith, B.},
howpublished = {SciDAC 2011, Denver, CO, USA, July 10-14},
month = {July},
year = {2011},
owner = {jkumar},
timestamp = {2011.06.20}
}
@conference{Marshall2011,
title = {Early Warning System for Identification and Monitoring of Disturbances to Forest Ecosystems},
author = {Aaron Marshall and Forrest M. Hoffman and Jitendra Kumar and William W. Hargrove and Richard T. Mills},
booktitle = {American Geophysical Union (AGU) Fall Meeting 2011, San Francisco, CA, December 5-9},
year = {2011},
month = {December},
abstract = {Forest ecosystems are susceptible to damage due to threat events like
wildfires, insect and disease attacks, extreme weather events, land
use change, and long-term climate change. Early identification of such
events is desired to devise and implement a protective response. The
mission of the USDA Forest Service is to sustain the health, diversity,
and productivity of the nation's forests. However, limited
resources for aerial surveys and ground-based inspections
are insufficient for monitoring the large areas
covered by the U.S. forests. The USDA Forest Service, Oak Ridge National
Laboratory, and NASA Stennis Space Center are developing an early warning system for the continuous
tracking and long-term monitoring of disturbances and responses in forest ecosystems using high
resolution satellite remote sensing data. Geospatiotemporal data mining
techniques were developed and applied to normalized difference vegetation
index (NDVI) products derived from the Moderate Resolution Imaging
Spectroradiometer (MODIS) MOD 13 data at 250 m resolution on
eight day intervals. Representative phenologically similar regions, or phenoregions, were developed for the
conterminous United States (CONUS) by applying a k-means clustering
algorithm to the NDVI data spanning the full eight years of the MODIS record. Annual changes in the phenoregions
were quantitatively analyzed to identify the significant changes in phenological behavior.
This methodology was successfully applied
for identification of various forest disturbance events, including wildfire,
tree mortality due to Mountain Pine Beetle, and other insect
infestation and diseases, as well as extreme events like storms
and hurricanes in the United States. Where possible, the results were validated
and quantitatively compared with aerial and ground-based survey data
available from different agencies. This system was able to identify
most of the disturbances reported by aerial and ground-based surveys,
and it also identified affected areas that were not covered by any of the
surveys. Analysis results and validation data will be presented.},
file = {Marshall2011.pdf:Publications/Marshall2011.pdf:PDF},
owner = {jkumar},
timestamp = {2011.09.11}
}
@conference{Mills2011c,
title = {A Statistical Methodology for Detecting and Monitoring Change in Forest Ecosystems Using Remotely Sensed Imagery},
author = {Richard Mills and Jitendra Kumar and Forrest Hoffman and William Hargrove and Joseph Spruce},
booktitle = {American Geophysical Union (AGU) Fall Meeting 2011, San Francisco, CA, December 5-9},
year = {2011},
month = {December},
abstract = {Variations in vegetation phenology, the annual temporal pattern of leaf
growth and senescence, can be a strong indicator of ecological change or
disturbance. However, phenology is also strongly influenced by seasonal,
interannual, and long-term trends in climate, making identification
of changes in forest ecosystems a challenge. Forest ecosystems are
are vulnerable to extreme weather events, insect and disease attacks,
wildfire, harvesting, and other land use change. Normalize difference
vegetation index (NDVI), a remotely sensed measure of greenness, provides
a proxy for phenology. NDVI for the conterminous United States (CONUS)
derived from the Moderate Resolution Spectroradiometer (MODIS) at 250
m resolution was used in this study to develop phenological signatures
of ecological regimes called phenoregions. By applying a quantitative
data mining technique to the NDVI measurements for every eight days over
the entire MODIS record, annual maps of phenoregions were developed.
This geospatiotemporal cluster analysis technique employs high performance
computing resources, enabling analysis of such very large data sets.
This technique produces a prescribed number of prototypical phenological
states to which every location belongs in any year. Analysis of the
shifts among phenological states yields information about responses
to interannual climate variability and, more importantly, changes in
ecosystem health due to disturbances. Moreover, a large change in the
phenological states occupied by a single location over time indicates
a significant disturbance or ecological shift. This methodology has
been applied for identification of various forest disturbance events,
including wildfire, tree mortality due to Mountain Pine Beetle, and other
insect infestation and diseases, as well as extreme events like storms
and hurricanes in the U.S. Presented will be results from analysis of
phenostate dynamics, along with disturbance and validation data.},
file = {Mills2011c.pdf:Publications/Mills2011c.pdf:PDF},
owner = {jkumar},
timestamp = {2011.09.11}
}
@conference{Mills2011a,
title = {Detection of forest threats via unsupervised geospatiotemporal data mining of remotely sensed phenology data},
author = {Richard T. Mills and Forrest M. Hoffman and Jitendra Kumar and William W. Hargrove},
booktitle = {US Internatinal Association of Landscape Ecology (US-IALE) Annual Symposium, Portland, Oregon, April 3-7},
year = {2011},
month = {April},
file = {:Publications/Mills_USIALE2011.pdf:PDF},
owner = {jkumar},
timestamp = {2010.11.30}
}
@conference{Mills2011b,
title = {Analysis of Phenological Signatures in Remote Sensing Data in the Southern Appalachians},
author = {Richard T. Mills and Jitendra Kumar and Forrest M. Hoffman and William W. Hargrove and Joe Spruce},
booktitle = {Southern Appalachian Man and the Biosphere (SAMAB) 21st Annual Conference 2011, Ashville, NC, November 15-17},
year = {2011},
abstract = {Forest ecosystems are vulnerable to extreme weather events, insect and disease attacks, wildfire, harvesting, and other land use change. Variations in vegetation phenology, the annual temporal pattern of leaf growth and senescence, can be a strong indicator of such disturbances. However, phenology is also strongly influenced by seasonal, interannual, and long-term trends in climate, making identification of changes in forest ecosystems a challenge. Normalized difference vegetation index (NDVI), a remotely sensed measure of greenness, provides a proxy for phenology. NDVI for the conterminous United States (CONUS) derived from the Moderate Resolution Spectroradiometer (MODIS) at 250 m resolution was used in this study to develop phenological signatures of ecological regimes called phenoregions. Using high performance computing, we have applied a quantitative data mining technique to the NDVI measurements for every eight days over the entire MODIS record to develop annual maps of phenoregion membership. These maps can be interpreted as incredibly detailed, dynamic maps of vegetation type with disturbance superimposed. Analysis of the shifts among phenological states yields information about responses to interannual climate variability and, more importantly, changes in ecosystem health due to disturbances. Moreover, a large change in the phenological states occupied by a single location over time indicates a significant disturbance or ecological shift. This methodology has been applied for identification of various forest disturbance events, including wildfire, tree mortality due to insect infestation and diseases, as well as extreme events like storms and hurricanes in the U.S. We will present the phenoregion maps we have calculated for the Southern Appalachians and show several examples of detection of forest disturbances via analysis of the phenostate dynamics.},
owner = {jkumar},
timestamp = {2011.09.11}
}
@comment{{jabref-entrytype: Presentations: req[] opt[author;howpublished;location;month;note;title;year]}}