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Laurence Lin

UNC IE | postdoc

 Recent Activity

ABSTRACT:

This is a supplementary data for the study "Accounting for adaptive water supply management when quantifying climate and landcover change vulnerability" by D. E. Gorelick,, L. Lin, H.B. Zeff, Y. Kim, J. M. Vose, J. W. Coulston, D. N. Wear, L. E. Band, P. M. Reed, and G. W. Characklis, as one of the publications supported by Water Sustainability and Climate NSF awarded project (EAR-1360442). The study article is submitted to the Water Resources Research (WRR) journal.

In this supplementary data package, users will find some spatially distributed maps (raster data) that were used by the study. We attached six projection realizations for all the data below, indicated by the number after letter 'r' in the file names.

1) projected 30-m Leaf Area Index (LAI) maps, derived from forest canopy information, e.g., vegetation community and vegetation density, are maintained by United States Department of Agriculture (USDA) Forest Service. General model and data descriptions are available at (https://www.fia.fs.fed.us/library/maps/index.php). Due to server storage size limit and data confidential, we reduced the accuracy of the LAI values from decimal to integer. Note that these LAI values are for the forested landcover, excluding the urban canopy LAI in urban area and the pasture/lawn LAI.

2) projected 30-m Landuse-Landcover (LULC) maps, produced by statistical spatial models by Martin et al. (2017) and Wear (2013) forecasting future forest landcover and urban expansion based on the economic scenarios (CMIP 5 RCP 6; Suttles et al. 2018) and planning development by the Triangle J Council of Governments (TJCOG). The LULC classes are the same as NLCD classes (https://www.mrlc.gov/data/legends/national-land-cover-database-2011-nlcd2011-legend), except all the forest LULC classes are lumped together as class ID 40.

3) projected regional climate time series from 1980 to 2090, derived from CMIP 5 RCP 6.0 projections and observed data from NC Climate Retrieval and Observation Network Of the Southeast Database. Climate time series include daily precipitation (mm), daily maximum air temperature (C), and daily minimum air temperature (C). We selected six GCMs (mostly U.S. GCMs and some international ones) for the projection, as well as a "consistent" projection that repeating historical climate pattern to the future as if "no climate change".

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ABSTRACT:

This is a supplementary data for the study "Accounting for adaptive water supply management when quantifying climate and landcover change vulnerability" by D. E. Gorelick,, L. Lin, H.B. Zeff, Y. Kim, J. M. Vose, J. W. Coulston, D. N. Wear, L. E. Band, P. M. Reed, and G. W. Characklis, as one of the publications supported by Water Sustainability and Climate NSF awarded project (EAR-1360442). The study article is submitted to the Water Resources Research (WRR) journal.

In this supplementary data package, users will find some spatially distributed maps (raster data) that were used by the study. We attached six projection realizations for all the data below, indicated by the number after letter 'r' in the file names.

1) projected 30-m Leaf Area Index (LAI) maps, derived from forest canopy information, e.g., vegetation community and vegetation density, are maintained by United States Department of Agriculture (USDA) Forest Service. General model and data descriptions are available at (https://www.fia.fs.fed.us/library/maps/index.php). Due to server storage size limit and data confidential, we reduced the accuracy of the LAI values from decimal to integer. Note that these LAI values are for the forested landcover, excluding the urban canopy LAI in urban area and the pasture/lawn LAI.

2) projected 30-m Landuse-Landcover (LULC) maps, produced by statistical spatial models by Martin et al. (2017) and Wear (2013) forecasting future forest landcover and urban expansion based on the economic scenarios (CMIP 5 RCP 6; Suttles et al. 2018) and planning development by the Triangle J Council of Governments (TJCOG). The LULC classes are the same as NLCD classes (https://www.mrlc.gov/data/legends/national-land-cover-database-2011-nlcd2011-legend), except all the forest LULC classes are lumped together as class ID 40.

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ABSTRACT:

This is a raw GIS dataset for BES Baisman Run catchment. it contains elevation, LULC, and soil data.

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ABSTRACT:

This is a zipped package containing raw GIS data and climate time series for RHESSys model setup. All data can be found on BES LTER website.

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ABSTRACT:

This zip file contains raw GIS files for RHESSys modeling. it has 30m DEM, NLCD, AWAS gage shapefile, and climate time series.

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 Contact

Resources
All 0
Collection 0
Composite Resource 0
Generic 0
Geographic Feature 0
Geographic Raster 0
HIS Referenced Time Series 0
Model Instance 0
Model Program 0
MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
Script Resource 0
SWAT Model Instance 0
Time Series 0
Web App 0
Generic Generic
RHESSys GIS variables (EJRV) for Coweeta Basin, NC
Created: Feb. 1, 2017, 6:39 p.m.
Authors: Laurence Lin

ABSTRACT:

The resource contains GIS maps for RHESSys eco-hydrological model. Maps includes elevation, roads, watershed gage locations, and remotely sensed LAI, as well as NLCD. All map resolutions are 10 m.

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Composite Resource Composite Resource
SWAS raw GIS
Created: March 7, 2019, 1:59 p.m.
Authors: Laurence Lin

ABSTRACT:

This zip file contains raw GIS files for RHESSys modeling. it has 30m DEM, NLCD, AWAS gage shapefile, and climate time series.

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Composite Resource Composite Resource
RHESSys GIS variables (EJRV) for Scotts Level Branch, MD
Created: April 17, 2019, 8:09 p.m.
Authors: Laurence Lin

ABSTRACT:

This is a zipped package containing raw GIS data and climate time series for RHESSys model setup. All data can be found on BES LTER website.

Show More
Composite Resource Composite Resource
BES Baisman Run GIS data
Created: Aug. 2, 2019, 1:47 p.m.
Authors: Lin, Laurence

ABSTRACT:

This is a raw GIS dataset for BES Baisman Run catchment. it contains elevation, LULC, and soil data.

Show More
Composite Resource Composite Resource

ABSTRACT:

This is a supplementary data for the study "Accounting for adaptive water supply management when quantifying climate and landcover change vulnerability" by D. E. Gorelick,, L. Lin, H.B. Zeff, Y. Kim, J. M. Vose, J. W. Coulston, D. N. Wear, L. E. Band, P. M. Reed, and G. W. Characklis, as one of the publications supported by Water Sustainability and Climate NSF awarded project (EAR-1360442). The study article is submitted to the Water Resources Research (WRR) journal.

In this supplementary data package, users will find some spatially distributed maps (raster data) that were used by the study. We attached six projection realizations for all the data below, indicated by the number after letter 'r' in the file names.

1) projected 30-m Leaf Area Index (LAI) maps, derived from forest canopy information, e.g., vegetation community and vegetation density, are maintained by United States Department of Agriculture (USDA) Forest Service. General model and data descriptions are available at (https://www.fia.fs.fed.us/library/maps/index.php). Due to server storage size limit and data confidential, we reduced the accuracy of the LAI values from decimal to integer. Note that these LAI values are for the forested landcover, excluding the urban canopy LAI in urban area and the pasture/lawn LAI.

2) projected 30-m Landuse-Landcover (LULC) maps, produced by statistical spatial models by Martin et al. (2017) and Wear (2013) forecasting future forest landcover and urban expansion based on the economic scenarios (CMIP 5 RCP 6; Suttles et al. 2018) and planning development by the Triangle J Council of Governments (TJCOG). The LULC classes are the same as NLCD classes (https://www.mrlc.gov/data/legends/national-land-cover-database-2011-nlcd2011-legend), except all the forest LULC classes are lumped together as class ID 40.

Show More
Composite Resource Composite Resource

ABSTRACT:

This is a supplementary data for the study "Accounting for adaptive water supply management when quantifying climate and landcover change vulnerability" by D. E. Gorelick,, L. Lin, H.B. Zeff, Y. Kim, J. M. Vose, J. W. Coulston, D. N. Wear, L. E. Band, P. M. Reed, and G. W. Characklis, as one of the publications supported by Water Sustainability and Climate NSF awarded project (EAR-1360442). The study article is submitted to the Water Resources Research (WRR) journal.

In this supplementary data package, users will find some spatially distributed maps (raster data) that were used by the study. We attached six projection realizations for all the data below, indicated by the number after letter 'r' in the file names.

1) projected 30-m Leaf Area Index (LAI) maps, derived from forest canopy information, e.g., vegetation community and vegetation density, are maintained by United States Department of Agriculture (USDA) Forest Service. General model and data descriptions are available at (https://www.fia.fs.fed.us/library/maps/index.php). Due to server storage size limit and data confidential, we reduced the accuracy of the LAI values from decimal to integer. Note that these LAI values are for the forested landcover, excluding the urban canopy LAI in urban area and the pasture/lawn LAI.

2) projected 30-m Landuse-Landcover (LULC) maps, produced by statistical spatial models by Martin et al. (2017) and Wear (2013) forecasting future forest landcover and urban expansion based on the economic scenarios (CMIP 5 RCP 6; Suttles et al. 2018) and planning development by the Triangle J Council of Governments (TJCOG). The LULC classes are the same as NLCD classes (https://www.mrlc.gov/data/legends/national-land-cover-database-2011-nlcd2011-legend), except all the forest LULC classes are lumped together as class ID 40.

3) projected regional climate time series from 1980 to 2090, derived from CMIP 5 RCP 6.0 projections and observed data from NC Climate Retrieval and Observation Network Of the Southeast Database. Climate time series include daily precipitation (mm), daily maximum air temperature (C), and daily minimum air temperature (C). We selected six GCMs (mostly U.S. GCMs and some international ones) for the projection, as well as a "consistent" projection that repeating historical climate pattern to the future as if "no climate change".

Show More