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Title: PISCEES Project for Land-Ice Modeling.

Abstract

Abstract not provided.

Authors:
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1375674
Report Number(s):
SAND2016-7867PE
646593
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the Reporter visit held August 17, 2016 in Livermore, CA.
Country of Publication:
United States
Language:
English

Citation Formats

Tezaur, Irina Kalashnikova. PISCEES Project for Land-Ice Modeling.. United States: N. p., 2016. Web.
Tezaur, Irina Kalashnikova. PISCEES Project for Land-Ice Modeling.. United States.
Tezaur, Irina Kalashnikova. 2016. "PISCEES Project for Land-Ice Modeling.". United States. doi:. https://www.osti.gov/servlets/purl/1375674.
@article{osti_1375674,
title = {PISCEES Project for Land-Ice Modeling.},
author = {Tezaur, Irina Kalashnikova},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 8
}

Conference:
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  • In North Carolina, emissions from vegetation comprise the bulk of the volatile organic compound (VOC) inventory. Estimation of these emissions will thus play a substantial role in the regulatory strategy suggested by modeling. The Urban Airshed Model (UAM), as available from the EPA, presently estimates biogenic emissions from land use/land cover data more than 20 years old. Recent data obtained from the US Forest Service`s periodic Forest Inventory and Assessment surveys have been gridded to the modeling domain. Emission estimates based upon these data will be compared with those from the older land use/land cover data set.
  • Land use/land cover (LULC) data are a vital component for nonpoint source pollution modeling. Most watershed hydrology and pollutant loading models use, in some capacity, LULC information to generate runoff and pollutant loading estimates. Simple equation methods predict runoff and pollutant loads using runoff coefficients or pollutant export coefficients that are often correlated to LULC type. Complex models use input variables and parameters to represent watershed characteristics and pollutant buildup and washoff rates as a function of LULC type. Whether using simple or complex models an accurate LULC dataset with an appropriate spatial resolution and level of detail is paramountmore » for reliable predictions. The study presented in this paper compared and evaluated several LULC dataset sources for application in urban environmental modeling. The commonly used USGS LULC datasets have coarser spatial resolution and lower levels of classification than other LULC datasets. In addition, the USGS datasets do not accurately represent the land use in areas that have undergone significant land use change during the past two decades. We performed a watershed modeling analysis of three urban catchments in Los Angeles, California, USA to investigate the relative difference in average annual runoff volumes and total suspended solids (TSS) loads when using the USGS LULC dataset versus using a more detailed and current LULC dataset. When the two LULC datasets were aggregated to the same land use categories, the relative differences in predicted average annual runoff volumes and TSS loads from the three catchments were 8 to 14% and 13 to 40%, respectively. The relative differences did not have a predictable relationship with catchment size.« less
  • Land use/land cover (LULC) data are a vital component for nonpoint source pollution modeling. Most watershed hydrology and pollutant loading models use, in some capacity, LULC information to generate runoff and pollutant loading estimates. Simple equation methods predict runoff and pollutant loads using runoff coefficients or pollutant export coefficients that are often correlated to LULC type. Complex models use input variables and parameters to represent watershed characteristics and pollutant buildup and washoff rates as a function of LULC type. Whether using simple or complex models an accurate LULC dataset with an appropriate spatial resolution and level of detail is paramountmore » for reliable predictions. The study presented in this paper compared and evaluated several LULC dataset sources for application in urban environmental modeling. The commonly used USGS LULC datasets have coarser spatial resolution and lower levels of classification than other LULC datasets. In addition, the USGS datasets do not accurately represent the land use in areas that have undergone significant land use change during the past two decades. We performed a watershed modeling analysis of three urban catchments in Los Angeles, California, USA to investigate the relative difference in average annual runoff volumes and total suspended solids (TSS) loads when using the USGS LULC dataset versus using a more detailed and current LULC dataset. When the two LULC datasets were aggregated to the same land use categories, the relative differences in predicted average annual runoff volumes and TSS loads from the three catchments were 8 to 14% and 13 to 40%, respectively. The relative differences did not have a predictable relationship with catchment size.« less
  • Abstract not provided.