peerreviewed_2005.bib

@conference{Kumar2005b,
  author = {Jitendra Kumar and Ashu Jain and Rajesh Srivastava},
  title = {Estimating grounwater pollution source location from observed breakthrough
	curve using neural networks},
  booktitle = {Proceedings of 2nd Indian International Conference on Artificial
	Intelligence (IICAI-05)},
  year = {2005},
  editor = {Bhanu Prasad},
  pages = {1004-1017},
  file = {pubs/Kumar_IICAI_2005.pdf},
  owner = {jkumar},
  timestamp = {2008.03.07}
}

@comment{{jabref-meta: selector_review:}}

@comment{{jabref-meta: selector_publisher:}}

@comment{{jabref-meta: selector_author:}}

@comment{{jabref-meta: selector_journal:}}

@comment{{jabref-meta: selector_keywords:}}

@inproceedings{Carr_CUG_20050516,
  author = {George R. Carr and Matthew J. Cordery and John B. Drake and
Michael W. Ham and Forrest M. Hoffman and Patrick H. Worley},
  title = {Porting and Performance of the {C}ommunity {C}limate {S}ystem {M}odel ({CCSM3}) on the {C}ray {X1}},
  booktitle = {Proceedings of the 2005 {C}ray {U}sers {G}roup ({CUG}) Conference},
  dates = {16--19 May 2005},
  location = {Albuquerque, New Mexico, USA},
  day = 16,
  month = may,
  year = 2005,
  abstract = {The Community Climate System Model (CCSM3) is the primary model for global climate research in the United States and is supported on a variety of computer systems. We present some of our porting experiences and describe the current performance of the CCSM3 on the Cray X1. We include the status of work in progress on other systems in the Cray product line.}
}

@article{Hargrove_LandscapeEcol_20050501,
  author = {William W. Hargrove and Forrest M. Hoffman and Rebecca A. Efroymson},
  title = {A Practical Map-Analysis Tool for Detecting Potential Dispersal Corridors},
  journal = landscapeecol,
  volume = 20,
  number = 4,
  pages = {361--373},
  doi = {10.1007/s10980-004-3162-y},
  day = 1,
  month = may,
  year = 2005,
  abstract = {We describe the Pathway Analysis Through Habitat (PATH) tool, which can predict the location of potential corridors of animal movement between patches of habitat within any map. The algorithm works by launching virtual entities that we call `walkers' from each patch of habitat in the map, simulating their travel as they journey through land cover types in the intervening matrix, and finally arrive at a different habitat `island.' Each walker is imbued with a set of user-specified habitat preferences that make its walking behavior resemble a particular animal species. Because the tool operates in parallel on a supercomputer, large numbers of walkers can be efficiently simulated. The importance of each habitat patch as a source or a sink for a species is calculated, consistent with existing concepts in the metapopulation literature. The manipulation of a series of contrived artificial landscapes demonstrates that the location of potential dispersal corridors and relative source and sink importance among patches can be purposefully altered in expected ways. Finally, potential dispersal corridors are predicted among remnant woodlots within three actual landscape maps.}
}

@article{Hoffman_EI_20050803,
  author = {Forrest M. Hoffman and William W. Hargrove and David J. Erickson and Robert J. Oglesby},
  title = {Using Clustered Climate Regimes to Analyze and Compare Predictions from Fully Coupled General Circulation Models},
  journal = ei,
  volume = 9,
  number = 10,
  pages = {1--27},
  doi = {10.1175/EI110.1},
  day = 3,
  month = aug,
  year = 2005,
  abstract = {Changes in Earth's climate in response to atmospheric greenhouse gas buildup impact the health of terrestrial ecosystems and the hydrologic cycle. The environmental conditions influential to plant and animal life are often mapped as ecoregions, which are land areas having similar combinations of environmental characteristics. This idea is extended to establish regions of similarity with respect to climatic characteristics that evolve through time using a quantitative statistical clustering technique called Multivariate Spatio-Temporal Clustering (MSTC). MSTC was applied to the monthly time series output from a fully coupled general circulation model (GCM) called the Parallel Climate Model (PCM). Results from an ensemble of five 99-yr Business-As-Usual (BAU) transient simulations from 2000 to 2098 were analyzed. MSTC establishes an exhaustive set of recurring climate regimes that form a ``skeleton'' through the ``observations'' (model output) throughout the occupied portion of the climate phase space formed by the characteristics being considered. MSTC facilitates direct comparison of ensemble members and ensemble and temporal averages since the derived climate regimes provide a basis for comparison. Moreover, by mapping all land cells to discrete climate states, the dynamic behavior of any part of the system can be studied by its time-varying sequence of climate state occupancy. MSTC is a powerful tool for model developers and environmental decision makers who wish to understand long, complex time series predictions of models. Strong predicted interannual trends were revealed in this analysis, including an increase in global desertification; a decrease in the cold, dry high-latitude conditions typical of North American and Asian winters; and significant warming in Antarctica and western Greenland.}
}

@article{Hoffman_IJHPCA_20050801,
  author = {Forrest M. Hoffman and Mariana Vertenstein and Hideyuki Kitabata and James B. {White III}},
  title = {Vectorizing the {C}ommunity {L}and {M}odel ({CLM})},
  journal = ijhpca,
  volume = 19,
  number = 3,
  pages = {247--260},
  doi = {10.1177/1094342005056113},
  day = 1,
  month = aug,
  year = 2005,
  abstract = {In this paper we describe our extensive efforts to rewrite the Community Land Model (CLM) so that it provides good vector performance on the Earth Simulator in Japan and the Cray X1 at Oak Ridge National Laboratory. We present the technical details of the old and new internal data structures, the required code reorganization, and the resulting performance improvements. We describe and compare the performance and scaling of the final CLM Version 3.0 (CLM3.0) on the IBM Power4, the Earth Simulator, and the Cray X1. At 64 processors, the performance of the model is similar on the IBM Power4, the Earth Simulator, and the Cray X1. However, the Cray X1 offers the best performance of all three platforms tested from 4 to 64 processors when OpenMP is used. Moreover, at low processor counts (16 or fewer), the model performs significantly better on the Cray X1 than on the other platforms. The vectorized version of CLM was publicly released by the National Center for Atmospheric Research as the standalone CLM3.0, as a part of the new Community Atmosphere Model Version 3.0 (CAM3.0), and as a component of the Community Climate System Model Version 3.0 (CCSM3.0) on June 23, 2004.}
}

@article{Saxon_EcolLett_20050101,
  author = {Earl Saxon and Barry Baker and William Hargrove and Forrest Hoffman and Chris Zganjar},
  title = {Mapping Environments at Risk Under Different Global Climate Change Scenarios},
  journal = ecollett,
  volume = 8,
  number = 1,
  pages = {53--60},
  doi = {10.1111/j.1461-0248.2004.00694.x},
  day = 1,
  month = jan,
  year = 2005,
  abstract = {All global circulation models based on Intergovernmental Panel on Climate Change (IPCC) scenarios project profound changes, but there is no consensus on how to map their environmental consequences. Our multivariate representation of environmental space combines stable topographic and edaphic attributes with dynamic climatic attributes. We divide that environmental space into 500 unique domains and map their current locations and their projected locations in 2100 under contrasting emissions scenarios. The environmental domains found across half the study area today disappear under the higher emissions scenario, but persist somewhere in it under the lower emissions scenario. Locations affected least and those affected most under each scenario are mapped. This provides an explicit framework for designing conservation networks to include both areas at least risk (potential refugia) and areas at greatest risk, where novel communities may form and where sentinel ecosystems can be monitored for signs of stress.}
}

@article{White_GRL_20050218,
  author = {Michael A. White and Forrest Hoffman and William W. Hargrove and Ramakrishna R. Nemani},
  title = {A Global Framework for Monitoring Phenological Responses to Climate Change},
  journal = grl,
  volume = 32,
  number = 4,
  pages = {L04705},
  doi = {10.1029/2004GL021961},
  day = 18,
  month = feb,
  year = 2005,
  abstract = {Remote sensing of vegetation phenology is an important method with which to monitor terrestrial responses to climate change, but most approaches include signals from multiple forcings, such as mixed phenological signals from multiple biomes, urbanization, political changes, shifts in agricultural practices, and disturbances. Consequently, it is difficult to extract a clear signal from the usually assumed forcing: climate change. Here, using global 8~km 1982 to 1999 Normalized Difference Vegetation Index (NDVI) data and an eight-element monthly climatology, we identified pixels whose wavelet power spectrum was consistently dominated by annual cycles and then created phenologically and climatically self-similar clusters, which we term phenoregions. We then ranked and screened each phenoregion as a function of landcover homogeneity and consistency, evidence of human impacts, and political diversity. Remaining phenoregions represented areas with a minimized probability of non-climatic forcings and form elemental units for long-term phenological monitoring.}
}