Laurence Lin

University of Virginia;University of North Carolina at Chapel Hill | Research Associate

Subject Areas: Forest Ecosystem, Nutrient cycle, Carbon budget, Stream Ecology

 Recent Activity

ABSTRACT:

RHESSys model was adapted for US east coast urban catchments. This model was set at 30 m spatial resolution and using 3-D soil profiles extracted from USDA SSURGO. Local climate input data were collected by BES study group and combined with quantile-bias corrected MD airport climate records to extend its period coverage. There are some historical model results and ecosystem stage files simulating the shift in landuse from agriculture to residential land.

Show More

ABSTRACT:

Baisman, MD, for RHESSys Model Input

Show More

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

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
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
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
Model Instance Resource Model Instance Resource
Baisman, MD, for RHESSys Model Input
Created: Jan. 17, 2021, 2:25 p.m.
Authors: Choi, Young-Don

ABSTRACT:

Baisman, MD, for RHESSys Model Input

Show More
Model Instance Resource Model Instance Resource

ABSTRACT:

RHESSys model was adapted for US east coast urban catchments. This model was set at 30 m spatial resolution and using 3-D soil profiles extracted from USDA SSURGO. Local climate input data were collected by BES study group and combined with quantile-bias corrected MD airport climate records to extend its period coverage. There are some historical model results and ecosystem stage files simulating the shift in landuse from agriculture to residential land.

Show More