peerreviewed_2006.bib

@article{Kumar_HydrologyJournal_2006,
  author = {Jitendra Kumar and Ashu Jain and Rajesh Srivastava},
  title = {Neural network based solutions for locating groundwater pollution
	source},
  journal = {Hydrology Journal, Indian Association of Hydrologists},
  year = {2006},
  volume = {29(1-2)},
  pages = {55-66},
  file = {pubs/Kumar_HydrologyJournal_2006.pdf},
  owner = {jkumar},
  timestamp = {2008.03.07},
  url = {http://www.indianjournals.com/ijor.aspx?target=ijor:hj&volume=29&issue=1and2&article=004}
}

@conference{Kumar2006d,
  author = {Jitendra Kumar and Manfred Ostrowski and Ashu Jain},
  title = {A {SWOT} analysis of {ANN} versus conceptual physically oriented
	modeling based on lysimeter data},
  booktitle = {Proceedings of 7th International Conference on Hydroinformatics,
	Nice, France},
  year = {2006},
  file = {pubs/Kumar_HIC_2006.pdf},
  owner = {jkumar},
  timestamp = {2008.03.07}
}

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@article{Dickinson_JClim_20060601,
  author = {Robert E. Dickinson and Keith W. Oleson and Gordon Bonan and Forrest Hoffman and Peter Thornton and Mariana Vertenstein and Zong-Liang Yang and Xubin Zeng},
  title = {The {C}ommunity {L}and {M}odel and Its Climate Statistics as a Component of the {C}ommunity {C}limate {S}ystem {M}odel},
  journal = jclim,
  volume = 19,
  number = 11,
  pages = {2302--2324},
  doi = {10.1175/JCLI3742.1},
  day = 1,
  month = jun,
  year = 2006,
  abstract = {Several multidecadal simulations have been carried out with the new version of the Community Climate System Model (CCSM). This paper reports an analysis of the land component of these simulations. Global annual averages over land appear to be within the uncertainty of observational datasets, but the seasonal cycle over land of temperature and precipitation appears to be too weak. These departures from observations appear to be primarily a consequence of deficiencies in the simulation of the atmospheric model rather than of the land processes. High latitudes of northern winter are biased sufficiently warm to have a significant impact on the simulated value of global land temperature. The precipitation is approximately doubled from what it should be at some locations, and the snowpack and spring runoff are also excessive. The winter precipitation over Tibet is larger than observed. About two-thirds of this precipitation is sublimated during the winter, but what remains still produces a snowpack that is very large compared to that observed with correspondingly excessive spring runoff. A large cold anomaly over the Sahara Desert and Sahel also appears to be a consequence of a large anomaly in downward longwave radiation; low column water vapor appears to be most responsible. The modeled precipitation over the Amazon basin is low compared to that observed, the soil becomes too dry, and the temperature is too warm during the dry season.}
}

@article{Hargrove_JGS_20060701,
  author = {William W. Hargrove and Forrest M. Hoffman and Paul F. Hessburg},
  title = {Mapcurves: A Quantitative Method for Comparing Categorical Maps},
  journal = jgs,
  volume = 8,
  number = 2,
  pages = {187--208},
  doi = {10.1007/s10109-006-0025-x},
  day = 1,
  month = jul,
  year = 2006,
  abstract = {We present Mapcurves, a quantitative goodness-of-fit (GOF) method that unambiguously shows the degree of spatial concordance between two or more categorical maps. Mapcurves graphically and quantitatively evaluate the degree of fit among any number of maps and quantify a GOF for each polygon, as well as the entire map. The Mapcurve method indicates a perfect fit even if all polygons in one map are comprised of unique sets of the polygons in another map, if the coincidence among map categories is absolute. It is not necessary to interpret (or even know) legend descriptors for the categories in the maps to be compared, since the degree of fit in the spatial overlay alone forms the basis for the comparison. This feature makes Mapcurves ideal for comparing maps derived from remotely sensed images. A translation table is provided for the categories in each map as an output. Since the comparison is category-based rather than cell-based, the GOF is resolution-independent. Mapcurves can be applied either to entire map categories or to individual raster patches or vector polygons. Mapcurves also have applications for quantifying the spatial uncertainty of particular map features.}
}

@article{Hoffman_JPConf_20060901,
  author = {Forrest M. Hoffman and Inez Fung and Jim Randerson and Peter Thornton and Jon Foley and Curtis Covey and Jasmin John and Samuel Levis and W. Mac Post and Mariana Vertenstein and Reto St\"ockli and Steve Running and Faith Ann Heinsch and David Erickson and John Drake},
  title = {Terrestrial Biogeochemistry in the {C}ommunity {C}limate {S}ystem {M}odel ({CCSM})},
  journal = jpconf,
  volume = 46,
  number = 1,
  pages = {363--369},
  doi = {10.1088/1742-6596/46/1/051},
  day = 1,
  month = sep,
  year = 2006,
  abstract = {Described here is the formulation of the CASA' biogeochemistry model of Fung, et al., which has recently been coupled to the Community Land Model Version 3 (CLM3) and the Community Climate System Model Version 3 (CCSM3). This model is presently being used for Coupled Climate/Carbon Cycle Model Intercomparison Project (C$^4$MIP) Phase 1 experiments. In addition, CASA' is one of three models---in addition to CN (Thornton, et al.) and IBIS (Thompson, et al.)---that are being run within CCSM to investigate their suitability for use in climate change predictions in a future version of CCSM. All of these biogeochemistry experiments are being performed on the Computational Climate Science End Station (Dr. Warren Washington, Principle Investigator) at the National Center for Computational Sciences at Oak Ridge National Laboratory.}
}