peerreviewed_2011.bib
@inproceedings{Hoffman2011a,
author = {Forrest M. Hoffman and J. Walter Larson and Richard Tran Mills and
Bj{\o}rn-Gustaf J. Brooks and Auroop R. Ganguly and William W. Hargrove
and Jian Huang and Jitendra Kumar and Ranga R. Vatsavai},
title = {{D}ata {M}ining in {E}arth {S}ystem {S}cience ({DMESS} 2011)},
booktitle = {Proceedings of the International Conference on Computational Science
({ICCS} 2011)},
year = {2011},
editor = {Mitsuhisa Sato and Satoshi Matsuoka and Peter M. Sloot and G. Dick
{van Albada} and Jack Dongarra},
volume = {4},
series = pcs,
pages = {1450--1455},
address = {Singapore},
month = jun,
publisher = {Elsevier},
abstract = {From field-scale measurements to global climate simulations and remote
sensing, the growing body of very large and long time series Earth
science data are increasingly difficult to analyze, visualize, and
interpret. Data mining, information theoretic, and machine learning
techniques---such as cluster analysis, singular value decomposition,
block entropy, Fourier and wavelet analysis, phase-space reconstruction,
and artificial neural networks---are being applied to problems of
segmentation, feature extraction, change detection, model-data comparison,
and model validation. The size and complexity of Earth science data
exceed the limits of most analysis tools and the capacities of desktop
computers. New scalable analysis and visualization tools, running
on parallel cluster computers and supercomputers, are required to
analyze data of this magnitude. This workshop will demonstrate how
data mining techniques are applied in the Earth sciences and describe
innovative computer science methods that support analysis and discovery
in the Earth sciences.},
dates = {1--3 June 2011},
day = {1},
doi = {10.1016/j.procs.2011.04.157},
file = {pubs/Hoffman_ICCS_2011.pdf},
issn = {1877-0509},
location = {Nanyang Technological University, Singapore},
owner = {jkumar},
timestamp = {2011.06.01}
}
@inproceedings{Kumar2011,
author = {Jitendra Kumar and Richard Tran Mills and Forrest M. Hoffman and
William W. Hargrove},
title = {Parallel $k$-Means Clustering for Quantitative Ecoregion Delineation
Using Large Data Sets},
booktitle = {Proceedings of the International Conference on Computational Science
({ICCS} 2011)},
year = {2011},
editor = {Mitsuhisa Sato and Satoshi Matsuoka and Peter M. Sloot and G. Dick
{van Albada} and Jack Dongarra},
volume = {4},
series = pcs,
pages = {1602--1611},
address = {Singapore},
month = jun,
publisher = {Elsevier},
abstract = {Identification of geographic ecoregions has long been of interest
to environmental scientists and ecologists for identifying regions
of similar ecological and environmental conditions. Such classifications
are important for predicting suitable species ranges, for stratification
of ecological samples, and to help prioritize habitat preservation
and remediation efforts. Hargrove and Hoffman [1] and [2] have developed
geographical spatio-temporal clustering algorithms and codes and
have successfully applied them to a variety of environmental science
domains, including ecological regionalization; environmental monitoring
network design; analysis of satellite-, airborne-, and ground-based
remote sensing, and climate model-model and model-measurement intercomparison.
With the advances in state-of-the-art satellite remote sensing and
climate models, observations and model outputs are available at increasingly
high spatial and temporal resolutions. Long time series of these
high resolution datasets are extremely large in size and growing.
Analysis and knowledge extraction from these large datasets are not
just algorithmic and ecological problems, but also pose a complex
computational problem. This paper focuses on the development of a
massively parallel multivariate geographical spatio-temporal clustering
code for analysis of very large datasets using tens of thousands
processors on one of the fastest supercomputers in the world.},
dates = {1--3 June 2011},
day = {1},
doi = {10.1016/j.procs.2011.04.173},
file = {pubs/Kumar_ICCS_2011.pdf},
issn = {1877-0509},
location = {Nanyang Technological University, Singapore},
owner = {jkumar},
timestamp = {2011.06.02}
}
@inproceedings{Mills2011,
author = {Richard Tran Mills and Forrest M. Hoffman and Jitendra Kumar and
William W. Hargrove},
title = {Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining
of Massive Data Sets for Identification of Forest Threats},
booktitle = {Proceedings of the International Conference on Computational Science
({ICCS} 2011)},
year = {2011},
editor = {Mitsuhisa Sato and Satoshi Matsuoka and Peter M. Sloot and G. Dick
{van Albada} and Jack Dongarra},
volume = {4},
series = pcs,
pages = {1612--1621},
address = {Singapore},
month = jun,
publisher = {Elsevier},
abstract = {We investigate methods for geospatiotemporal data mining of multi-year
land surface phenology data (250 m$^2$ Normalized Difference Vegetation
Index (NDVI) values derived from the Moderate Resolution Imaging
Spectrometer (MODIS) in this study) for the conterminous United States
(CONUS) as part of an early warning system for detecting threats
to forest ecosystems. The approaches explored here are based on $k$-means
cluster analysis of this massive data set, which provides a basis
for defining the bounds of the expected or ``normal'' phenological
patterns that indicate healthy vegetation at a given geographic location.
We briefly describe the computational approaches we have used to
make cluster analysis of such massive data sets feasible, describe
approaches we have explored for distinguishing between normal and
abnormal phenology, and present some examples in which we have applied
these approaches to identify various forest disturbances in the CONUS.},
dates = {1--3 June 2011},
day = {1},
doi = {10.1016/j.procs.2011.04.174},
file = {pubs/Mills_ICCS_2011.pdf},
issn = {1877-0509},
location = {Nanyang Technological University, Singapore},
owner = {jkumar},
timestamp = {2011.06.01}
}
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@article{Alexandrov_EMS_20110301,
author = {G. A. Alexandrov and D. Ames and G. Bellocchi and M. Bruen and N. Crout and M. Erechtchoukova and A. Hildebrandt and F. Hoffman and C. Jackisch and P. Khaiter and G. Mannina and T. Matsunaga and S. T. Purucker and M. Rivington and L. Samaniego},
title = {Technical Assessment and Evaluation of Environmental Models and Software: Letter to the Editor},
journal = ems,
volume = 26,
number = 3,
note = {{T}hematic issue on the assessment and evaluation of environmental models and software},
pages = {328--336},
doi = {10.1016/j.envsoft.2010.08.004},
day = 1,
month = mar,
year = 2011,
abstract = {This letter details the collective views of a number of independent researchers on the technical assessment and evaluation of environmental models and software. The purpose is to stimulate debate and initiate action that leads to an improved quality of model development and evaluation, so increasing the capacity for models to have positive outcomes from their use. As such, we emphasize the relationship between the model evaluation process and credibility with stakeholders (including funding agencies) with a view to ensure continued support for modelling efforts.
Many journals, including EM\&S, publish the results of environmental modelling studies and must judge the work and the submitted papers based solely on the material that the authors have chosen to present and on how they present it. There is considerable variation in how this is done with the consequent risk of considerable variation in the quality and usefulness of the resulting publication. Part of the problem is that the review process is reactive, responding to the submitted manuscript. In this letter, we attempt to be proactive and give guidelines for researchers, authors and reviewers as to what constitutes best practice in presenting environmental modelling results. This is a unique contribution to the organisation and practice of model-based research and the communication of its results that will benefit the entire environmental modelling community. For a start, our view is that the community of environmental modellers should have a common vision of minimum standards that an environmental model must meet. A common vision of what a good model should be is expressed in various guidelines on Good Modelling Practice. The guidelines prompt modellers to codify their practice and to be more rigorous in their model testing. Our statement within this letter deals with another aspect of the issue---it prompts professional journals to codify the peer-review process. Introducing a more formalized approach to peer-review may discourage reviewers from accepting invitations to review given the additional time and labour requirements. The burden of proving model credibility is thus shifted to the authors. Here we discuss how to reduce this burden by selecting realistic evaluation criteria and conclude by advocating the use of standardized evaluation tools as this is a key issue that needs to be tackled.}
}
@article{Shi_GRL_20110423,
author = {Xiaoying Shi and Jiafu Mao and Peter E. Thornton and Forrest M. Hoffman and Wilfred M. Post},
title = {The Impact of Climate, {CO}$_2$, Nitrogen Deposition, and Land Use Change on Simulated Contemporary Global River Flow},
journal = grl,
volume = 38,
number = 8,
pages = {L08704},
doi = {10.1029/2011GL046773},
day = 23,
month = apr,
year = 2011,
abstract = {We investigated how climate, rising atmospheric CO$_2$ concentration, increasing anthropogenic nitrogen deposition and land use change influenced continental river flow over the period 1948--2004 using the Community Land Model version 4 (CLM4) with coupled river transfer model (RTM), a global river routing scheme. The model results indicate that the global mean river flow shows significant decreasing trend and climate forcing likely functions as the dominant controller of the downward trend during the study period. Nitrogen deposition and land use change account for about 5\% and 2.5\% of the decrease in simulated global scale river flow, respectively, while atmospheric CO$_2$ accounts for an upward trend. However, the relative role of each driving factor is heterogeneous across regions in our simulations. The trend in river flow for the Amazon River basin is primarily explained by CO$_2$, while land use change accounts for 27.4\% of the downward trend in river flow for the Yangtze rive basin. Our simulations suggest that to better understand the trends of river flow, it is not only necessary to take into account the climate, but also to consider atmospheric composition, carbon-nitrogen interaction and land use change, particularly for regional scales.}
}
@inproceedings{Sisneros_ICCS_20110601,
author = {Robert Sisneros and Jian Huang and George Ostrouchov and Forrest Hoffman},
title = {Visualizing Life Zone Boundary Sensitivities Across Climate Models and Temporal Spans},
booktitle = {Proceedings of the International Conference on Computational Science ({ICCS} 2011)},
editor = {Mitsuhisa Sato and Satoshi Matsuoka and Peter M. Sloot and G. Dick {van Albada} and Jack Dongarra},
publisher = {Elsevier},
address = {Amsterdam},
series = pcs,
dates = {1--3 June 2011},
location = {Nanyang Technological University, Singapore},
volume = 4,
pages = {1582--1591},
doi = {10.1016/j.procs.2011.04.171},
issn = {1877-0509},
day = 1,
month = jun,
year = 2011,
abstract = {Life zones are a convenient and quantifiable method for delineating areas with similar plant and animal communities based on bioclimatic conditions. Such ecoregionalization techniques have proved useful for defining habitats and for studying how these habitats may shift due to environmental change. The ecological impacts of climate change are of particular interest. Here we show that visualizations of the geographic projection of life zones may be applied to the investigation of potential ecological impacts of climate change using the results of global climate model simulations. Using a multi-factor classification scheme, we show how life zones change over time based on quantitative model results into the next century. Using two straightforward metrics, we identify regions of high sensitivity to climate changes from two global climate simulations under two different greenhouse gas emissions scenarios. Finally, we identify how preferred human habitats may shift under these scenarios. We apply visualization methods developed for the purpose of displaying multivariate relationships within data, especially for situations that involve a large number of concurrent relationships. Our method is based on the concept of multivariate classification, and is implemented directly in VisIt, a production quality visualization package.}
}