GeoServer Web Map Service

hillshade_sca (hillshade_sca)

Layer-Group type layer: hillshade_sca

Haiti Admin Boundaries, Limite Departement (haitiegov:Limite-Departement)

Adaptation Areas (nyserda:adaptation_areas)

2nd Std. Depth 5-15 CM (afsis:afsis-BLD-1km-sd2-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for bulk density in tones per cubic-meter for the second standard depth (5-15 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

4th Std. Depth 30-60 CM (afsis:afsis-BLD-1km-sd4-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for bulk density in tones per cubic-meter for the fourth standard depth (30-60 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

6th Std. Depth 100-200 CM (afsis:afsis-BLD-1km-sd6-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for bulk density in tones per cubic-meter for the sixth standard depth (100-200 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

2nd Std. Depth 5-15 CM (afsis:afsis-CEC-1km-sd2-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for cation exchange capacity (soil) in cmol/kg for the second standard depth (5-15 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

4th Std. Depth 30-60 CM (afsis:afsis-CEC-1km-sd4-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for cation exchange capacity (soil) in cmol/kg for the forth standard depth (30-60 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

6th Std. Depth 100-200 CM (afsis:afsis-CEC-1km-sd6-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for cation exchange capacity (soil) in cmol/kg for the sixth standard depth (100-200 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

2nd Std. Depth 5-15 CM (afsis:afsis-CLYPPT-1km-sd2-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for clay content (<2 µm) in percent for the second standard depth (5-15 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

4th Std. Depth 30-60 CM (afsis:afsis-CLYPPT-1km-sd4-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for clay content (<2 µm) in percent for the forth standard depth (30-60 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

6th Std. Depth 100-200 CM (afsis:afsis-CLYPPT-1km-sd6-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for clay content (<2 µm) in percent for the sixth standard depth (100-200 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

2nd Std. Depth 5-15 CM (afsis:afsis-ORCDRC-1km-sd2-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for soil organic carbon in permilles (g/kg) for the second standard depth (5-15 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

4th Std. Depth 30-60 CM (afsis:afsis-ORCDRC-1km-sd4-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for soil organic carbon in permilles (g/kg) for the forth standard depth (30-60 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

6th Std. Depth 100-200 CM (afsis:afsis-ORCDRC-1km-sd6-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for soil organic carbon in permilles (g/kg) for the sixth standard depth (100-200 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

2nd Std. Depth 5-15 CM (afsis:afsis-PHIHO5-1km-sd2-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for soil pH 1:5 soil/water solution for the second standard depth (5-15 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

4th Std. Depth 30-60 CM (afsis:afsis-PHIHO5-1km-sd4-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for soil pH 1:5 soil/water solution for the fourth standard depth (30-60 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

6th Std. Depth 100-200 CM (afsis:afsis-PHIHO5-1km-sd6-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for soil pH 1:5 soil/water solution for the sixth standard depth (100-200 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

2nd Std. Depth 5-15 CM (afsis:afsis-SLTPPT-1km-sd2-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for silt content (2-50 µm) in percent for the second standard depth (5-15 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

4th Std. Depth 30-60 CM (afsis:afsis-SLTPPT-1km-sd4-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for silt content (2-50 µm) in percent for the forth standard depth (30-60 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

6th Std. Depth 100-200 CM (afsis:afsis-SLTPPT-1km-sd6-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for silt content (2-50 µm) in percent for the sixth standard depth (100-200 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

2nd Std. Depth 5-15 CM (afsis:afsis-SNDPPT-1km-sd2-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for sand content (50-2000 µm) in percent for the second standard depth (5-15 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

4th Std. Depth 30-60 CM (afsis:afsis-SNDPPT-1km-sd4-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for sand content (50-2000 µm) in percent for the forth standard depth (30-60 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

6th Std. Depth 100-200 CM (afsis:afsis-SNDPPT-1km-sd6-M)

This directory contains soil property maps of Africa at 1 km resolution including the predicted mean value for sand content (50-2000 µm) in percent for the sixth standard depth (100-200 cm). Inputs: Africa Soil Profile DB, WorldGrids 1 km covariates Period (temporal coverage approximate): 1950-2005 Spatial resolution (covariates): 1 km Spatial resolution predictions (support size): point support Data license (IP policy): Attribution-ShareAlike CC BY-SA Citation:ISRIC – World Soil Information, 2013. Africa soil property maps at 1 km. Available for download at www.isric.org

Tropical Rainfall Measuring Mission (TRMM) Number of Rainy Days per Year Long-Term Average (afsis:afsis-TRMM-average-rainy-days)

This directory contains the Africa continent-wide rasters of the average number of rainy days per year using the daily precipitation observations produced by NASA's Tropical Rainfall Measuring Mission hosted at Columbia University’s International Research Institute for Climate and Society (IRI) Data Library. The geotiff files have been created for each year from 1998 to 2012 with a .25 resolution in Lambert Azimuthal Equal Area projection.

WorldClim: BIO12 Precipitation Long Term Average (mm/month)) (afsis:afsis-WorldClim-BIO12-annual-precipiation)

This directory contains The Africa continent-wide BIO12 Precipitation Time Series Average data set was produced by the Africa Soil Information Service (AfSIS) using the bioclimatic variables by WorldClim. The pre-processed data from WorldClim is at 1km-squared spatial resolution with a temporal range of approximately 1950-2000.

Albedo White Sky Near Infrared (afsis:afsis-albedo-white-sky-nir)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: White Sky Albedo Near Infrared Band Long Term Average and Variance. Palisades, NY: Center for International Earth Science Information Network (CIESIN), Columbia University. http://www.africasoils.net/data/datasets. Accessed DAY MONTH YEAR

Area Equipped for Irrigation (%) (afsis:afsis-area-equipped-for-irrigation)

SRTM Digital Elevation Model (DEM)-90m (afsis:afsis-digital-elevation-model)

Shuttle RADAR Topographic Mission (SRTM) derivative: Digital Elevation Model (DEM)

Enhanced Vegetation Index (afsis:afsis-enhanced-vegetation-index)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Enhanced Vegetation Index (EVI) Long Term and Monthly Averages. Palisades, NY: Center for International Earth Science Information Network (CIESIN), Columbia University. http://www.africasoils.net/data/datasets. Accessed DAY MONTH YEAR.

Field Locations (afsis:afsis-field-locations)

Global Carbon Biomass (tonnes of biomass carbon/hectare) (afsis:afsis-global-carbon-biomass)

Ruesch, Aaron, and Holly K. Gibbs. 2008. New IPCC Tier-1 Global Biomass Carbon Map For the Year 2000. Available online from the Carbon Dioxide Information Analysis Center [http://cdiac.ornl.gov], Oak Ridge National Laboratory, Oak Ridge, Tennessee. http://cdiac.ornl.gov/epubs/ndp/global_carbon/carbon_documentation.html

Land Cover-MERIS (afsis:afsis-land-cover-meris)

Land cover maps are categorical-type maps, commonly derived using semi-automated methods and remote sensing images as the main input. There are at least four global land cover mapping projects in the world where such data can be found (they differ in legends, resolution, temporal coverage etc). A Global Land Cover map for the year 2000 (GLC2000) at 1 km resolution is distributed by the Joint Research Centre in Italy (Bartholome et al., 2002). A slightly outdated (1998) global map of land cover is provided by the AVHRR Global Land Cover Classification, provided at resolutions of 1 and 8 km (Hansen et al. 2000). International Steering Committee for Global Mapping provides access to the Global Land Cover by National Mapping Organizations (GLCNMO) map, produced using MODIS data observed in 2003. European Space Agency has recently released the GlobCover Land Cover version V2 dataset, produced using the ENVISAT MERIS images. So far, this is the highest resolution (300 meters) Global Land Cover product in the world. The forth important source of land cover data is the MODIS12C1 Land Cover Type Yearly L3 Global product (available in resolution from 500 m to 0.05 arcdegrees). The advantage of using the MODIS Land cover maps (17 land cover classes defined by the International Geosphere Biosphere Programme - IGBP) is that this is a temporal dataset so that one can also derive various change indices and quantify the land cover dynamics (Friedl et al. 2002).

Land Surface Temperature, Night (Celsius) (afsis:afsis-land-surface-temperature-night)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Land Surface Temperature (LST) Night Long-term and Monthly Averages, and Variance. Palisades, NY: Center for International Earth Science Information Network (CIESIN), Columbia University. http://www.africasoils.net/data/datasets. Accessed DAY MONTH YEAR.

Leaf Area Index (m2 plant/m2 ground) (afsis:afsis-leaf-area-index)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Leaf Area Index (LAI) Long-term Average, Variance, and Standard Deviation. Palisades, NY: Center for International Earth Science Information Network (CIESIN), Columbia University. http://www.africasoils.net/data/datasets. Accessed DAY MONTH YEAR

Mid Infrared Reflectance (afsis:afsis-mid-infrared-reflectance)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Mid-infrared (MIR) Reflectance (Band 7) Long Term and Monthly Averages. Palisades, NY: Center for International Earth Science Information Network (CIESIN), Columbia University. http://www.africasoils.net/data/datasets. Accessed DAY MONTH YEAR.

Near Infrared Reflectance (afsis:afsis-near-infrared-reflectance)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Near-infrared (NIR) Reflectance (Band 2) Long Term and Monthly Averages. Palisades, NY: Center for International Earth Science Information Network (CIESIN), Columbia University. http://www.africasoils.net/data/datasets. Accessed DAY MONTH YEAR.

Normalized Difference Vegetation Index (afsis:afsis-normalized-difference-vegetation-index)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Normalized Difference Vegetation Index (NDVI) Long Term and Monthly Averages. Palisades, NY: Center for International Earth Science Information Network (CIESIN), Columbia University. http://www.africasoils.net/data/datasets. Accessed DAY MONTH YEAR.

Red Reflectance (afsis:afsis-red-reflectance)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Red Reflectance (Band 1) Long Term and Monthly Averages. Palisades, NY: Center for International Earth Science Information Network (CIESIN), Columbia University. http://www.africasoils.net/data/datasets. Accessed DAY MONTH YEAR.

Sentinel Sites (afsis:afsis-sentinel-landscapes)

Specific Catchment Area (SCA)-90m (afsis:afsis-specific-catchment-area)

Shuttle RADAR Topographic Mission (SRTM) derivative: Specific Catchment Area (SCA)

Tree Cover (%) (afsis:afsis-tree-cover)

DeFries, R., M. Hansen, J.R.G. Townshend, A.C. Janetos, and T.R. Loveland (2000), 1 Kilometer Tree Cover Continuous Fields, 1.0, Department of Geography, University of Maryland, College Park, Maryland, 1992-1993.

Airports (nyserda:airports)

The Airports data set displays the locations of all of the airports in the counties bordering the Hudson River. The data was obtained from NYS GIS Clearinghouse at http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=929 in April 2014.

artibonite_mask (haitiegov:artibonite-mask)

Bangladesh Migration from Khanpara (care:bgd-khanpara-flow)

Bangladesh Study Villages (care:bgd-villages)

Bridges (nyserda:bridges)

Vector file of bridges compiled from orthoimagery and NYSDOT Bridge Data Management System (BDMS) and other sources. The file includes all bridges that carry or cross a public road. Information on NYSDOT's bridge inventory process and the attribute fields in this geodatabase is available from NYSDOT's Bridge Inventory Manual at https://www.dot.ny.gov/divisions/engineering/structures/manuals/bridge-inventory-manual.

Bus stations (nyserda:bus_stations)

The Bus Station Location data set displays the locations of all bus stations in the study area. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1115 in April 2014.

Bean (care:care-bean-binary)

Cowpeas (care:care-cowpeas-binary)

Groundnut (care:care-groundnut-binary)

Jute (care:care-jute-binary)

Irrigated Cultivated Land in 2000 (care:care-land-cover-irrigated-2000)

Landslide Risk (care:care-landslide-risk)

Millet (care:care-millet-binary)

Pea (care:care-pea-binary)

Potato (care:care-potato-binary)

Annual Average of Precipitation 1960-1990 (care:care-precipitation-annual-average-10minute-1960-90)

Sesame (care:care-sesame-binary)

Soil Nutrient Availability (care:care-soil-quality-nutrient-availability)

Soybean (care:care-soybean-binary)

Sweet potato (care:care-sweetpotato-binary)

Yam (care:care-yam-binary)

Average Daily Maximum Temperature 1901 to 2002 (climate1stop:climate1stop-average-daily-maximum-temperature-1901-2002)

Average Diurnal Temperature Range 1901 to 2002 (climate1stop:climate1stop-average-diurnal-temperature-range-1901-2002)

Average Vapor Pressure 1901 to 2002 (climate1stop:climate1stop-average-vapor-pressure-1901-2002)

Potential Evapotranspiration 2031 to 2040 Dry Model (climate1stop:climate1stop-potential-evapotranspiration-dry-model-2031-2040)

Potential Evapotranspiration 2031 to 2040 Mid Model (climate1stop:climate1stop-potential-evapotranspiration-mid-model-2031-2040)

Potential Evapotranspiration 2031 to 2040 Wet Model (climate1stop:climate1stop-potential-evapotranspiration-wet-model-2031-2040)

Potential Evapotranspiration Yearly Average 1961 to 1990 (climate1stop:climate1stop-potential-evapotranspiration-yearly-average-1961-1990)

Precipitation 2051 to 2060 Dry Model (climate1stop:climate1stop-precipitation-dry-model-2051-2060)

Precipitation 2051 to 2060 Mid Model (climate1stop:climate1stop-precipitation-mid-model-2051-2060)

Precipitation 2051 to 2060 Wet Model (climate1stop:climate1stop-precipitation-wet-model-2051-2060)

Runoff 2031 to 2040 Dry Model (climate1stop:climate1stop-runoff-dry-model-2031-2040)

Runoff 2031 to 2040 Mid Model (climate1stop:climate1stop-runoff-mid-model-2031-2040)

Runoff 2031 to 2040 Wet Model (climate1stop:climate1stop-runoff-wet-model-2031-2040)

Runoff Yearly Average 1961 to 1990 (climate1stop:climate1stop-runoff-yearly-average-1961-1990)

Soil Moisture 2051 to 2060 Dry Model (climate1stop:climate1stop-soil-moisture-dry-model-2051-2060)

Soil Moisture 2051 to 2060 Mid Model (climate1stop:climate1stop-soil-moisture-mid-model-2051-2060)

Soil Moisture 2051 to 2060 Wet Model (climate1stop:climate1stop-soil-moisture-wet-model-2051-2060)

Temperature 2031 to 2040 Dry Model (climate1stop:climate1stop-temperature-dry-model-2031-2040)

Temperature 2031 to 2040 Mid Model (climate1stop:climate1stop-temperature-mid-model-2031-2040)

Temperature 2031 to 2040 Wet Model (climate1stop:climate1stop-temperature-wet-model-2031-2040)

Temperature Yearly Average 1961 to 1990 (climate1stop:climate1stop-temperature-yearly-average-1961-1990)

country-esri101-haiti-mask (haitiegov:country-esri101-haiti-mask)

Indicateurs des menages: Agriculture (haiti:csi-hh-indicators-agriculture)

Household Indicators: Agriculture

Indicateurs des menages: Energy (haiti:csi-hh-indicators-energy)

Household Indicators: Energy

Indicateurs des menages: Food Security (haiti:csi-hh-indicators-foodsecurity)

Household Indicators: Food Security

Indicateurs des menages: Sante (haiti:csi-hh-indicators-health)

Household Indicators: Sante

Indicateurs des menages: Nutrition (haiti:csi-hh-indicators-nutrition)

Household Indicators: Nutrition

Densite de la population 2003 (haiti:csi-population-2003)

Haiti Population Density 2003

Dams (nyserda:dams)

This dataset is used to show the location of dams in New York State's inventory of dams and lists selected attributes of each dam. A point file to show the location of dams in the New York State Inventory of Dams. The data was obtained from New York State at http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1130 in April 2014.

egov-sample (haitiegov:egov-sample)

Emergency operations centers (nyserda:emergency_operations_centers)

The Emergency operations centers data set includes the locations of 76 agencies and their contacts.

100 Year Flood (1% chance each year) (fib:fib-100yrflood-closed)

10 Year Flood (10% chance each year) (fib:fib-10yrflood-closed)

500 Year Flood (0.2% chance each year) (fib:fib-500yrflood-closed)

Buildings - Commercial (fib:fib-buildings-commercial-all)

Buildings - Exempt (fib:fib-buildings-exempt)

Buildings - Mixed Residential Commercial (fib:fib-buildings-mixed)

Buildings - Residential 1 Family (fib:fib-buildings-r1)

Buildings - Residential 7 or more Units (fib:fib-buildings-r7)

Bus Stops (fib:fib-bus-stops)

Corner Stores (fib:fib-corner-stores)

Grocery Stores (fib:fib-grocery)

Hospitals (fib:fib-hospitals-extent)

Long Term Care Facilities (fib:fib-longterm-care)

Flood Pathways (fib:fib-pathways)

Public Housing (fib:fib-public-housing)

Roads (fib:fib-roads)

Study Area Mask (fib:fib-study-area-mask)

Train Stations (fib:fib-trains-stations)

Mean Higher High Water - MHHW (nyserda:flood_00_0000)

This layer represents the mean high high water extent

Flood Estimate - 0in Sea Level Rise 10yr Storm (nyserda:flood_00_0010)

The estimated flood extent with 0 inches of Sea Level Rise and a 10 year Storm Return Period

Flood Estimate - 0in Sea Level Rise 50yr Storm (nyserda:flood_00_0050)

The estimated flood extent with 0 inches of Sea Level Rise and a 50 year Storm Return Period

Flood Estimate - 0in Sea Level Rise 200yr Storm (nyserda:flood_00_0200)

The estimated flood extent with 0 inches of Sea Level Rise and a 200 year Storm Return Period

Flood Estimate - 0in Sea Level Rise 1000yr Storm (nyserda:flood_00_1000)

The estimated flood extent with 0 inches of Sea Level Rise and a 1000 year Storm Return Period

Flood Estimate - 6in Sea Level Rise 5yr Storm (nyserda:flood_06_0005)

The estimated flood extent with 6 inches of Sea Level Rise and a 5 year Storm Return Period

Flood Estimate - 6in Sea Level Rise 20yr Storm (nyserda:flood_06_0020)

The estimated flood extent with 6 inches of Sea Level Rise and a 20 year Storm Return Period

Flood Estimate - 6in Sea Level Rise 100yr Storm (nyserda:flood_06_0100)

The estimated flood extent with 6 inches of Sea Level Rise and a 100 year Storm Return Period

Flood Estimate - 6in Sea Level Rise 500yr Storm (nyserda:flood_06_0500)

The estimated flood extent with 6 inches of Sea Level Rise and a 500 year Storm Return Period

Flood Estimate - 12in Sea Level Rise 0yr Storm (nyserda:flood_12_0000)

The estimated flood extent with 12 inches of Sea Level Rise and a 0 year Storm Return Period

Flood Estimate - 12in Sea Level Rise 10yr Storm (nyserda:flood_12_0010)

The estimated flood extent with 12 inches of Sea Level Rise and a 10 year Storm Return Period

Flood Estimate - 12in Sea Level Rise 50yr Storm (nyserda:flood_12_0050)

The estimated flood extent with 12 inches of Sea Level Rise and a 50 year Storm Return Period

Flood Estimate - 12in Sea Level Rise 200yr Storm (nyserda:flood_12_0200)

The estimated flood extent with 12 inches of Sea Level Rise and a 200 year Storm Return Period

Flood Estimate - 12in Sea Level Rise 1000yr Storm (nyserda:flood_12_1000)

The estimated flood extent with 12 inches of Sea Level Rise and a 1000 year Storm Return Period

Flood Estimate - 18in Sea Level Rise 5yr Storm (nyserda:flood_18_0005)

The estimated flood extent with 18 inches of Sea Level Rise and a 5 year Storm Return Period

Flood Estimate - 18in Sea Level Rise 20yr Storm (nyserda:flood_18_0020)

The estimated flood extent with 18 inches of Sea Level Rise and a 20 year Storm Return Period

Flood Estimate - 18in Sea Level Rise 100yr Storm (nyserda:flood_18_0100)

The estimated flood extent with 18 inches of Sea Level Rise and a 100 year Storm Return Period

Flood Estimate - 18in Sea Level Rise 500yr Storm (nyserda:flood_18_0500)

The estimated flood extent with 18 inches of Sea Level Rise and a 500 year Storm Return Period

Flood Estimate - 24in Sea Level Rise 0yr Storm (nyserda:flood_24_0000)

The estimated flood extent with 24 inches of Sea Level Rise and a 0 year Storm Return Period

Flood Estimate - 24in Sea Level Rise 10yr Storm (nyserda:flood_24_0010)

The estimated flood extent with 24 inches of Sea Level Rise and a 10 year Storm Return Period

Flood Estimate - 24in Sea Level Rise 50yr Storm (nyserda:flood_24_0050)

The estimated flood extent with 24 inches of Sea Level Rise and a 50 year Storm Return Period

Flood Estimate - 24in Sea Level Rise 200yr Storm (nyserda:flood_24_0200)

The estimated flood extent with 24 inches of Sea Level Rise and a 200 year Storm Return Period

Flood Estimate - 24in Sea Level Rise 1000yr Storm (nyserda:flood_24_1000)

The estimated flood extent with 24 inches of Sea Level Rise and a 1000 year Storm Return Period

Flood Estimate - 30in Sea Level Rise 5yr Storm (nyserda:flood_30_0005)

The estimated flood extent with 30 inches of Sea Level Rise and a 5 year Storm Return Period

Flood Estimate - 30in Sea Level Rise 20yr Storm (nyserda:flood_30_0020)

The estimated flood extent with 30 inches of Sea Level Rise and a 20 year Storm Return Period

Flood Estimate - 30in Sea Level Rise 100yr Storm (nyserda:flood_30_0100)

The estimated flood extent with 30 inches of Sea Level Rise and a 100 year Storm Return Period

Flood Estimate - 30in Sea Level Rise 500yr Storm (nyserda:flood_30_0500)

The estimated flood extent with 30 inches of Sea Level Rise and a 500 year Storm Return Period

Flood Estimate - 36in Sea Level Rise 0yr Storm (nyserda:flood_36_0000)

The estimated flood extent with 36 inches of Sea Level Rise and a 0 year Storm Return Period

Flood Estimate - 36in Sea Level Rise 10yr Storm (nyserda:flood_36_0010)

The estimated flood extent with 36 inches of Sea Level Rise and a 10 year Storm Return Period

Flood Estimate - 36in Sea Level Rise 50yr Storm (nyserda:flood_36_0050)

The estimated flood extent with 36 inches of Sea Level Rise and a 50 year Storm Return Period

Flood Estimate - 36in Sea Level Rise 200yr Storm (nyserda:flood_36_0200)

The estimated flood extent with 36 inches of Sea Level Rise and a 200 year Storm Return Period

Flood Estimate - 36in Sea Level Rise 1000yr Storm (nyserda:flood_36_1000)

The estimated flood extent with 36 inches of Sea Level Rise and a 1000 year Storm Return Period

Flood Estimate - 48in Sea Level Rise 5yr Storm (nyserda:flood_48_0005)

The estimated flood extent with 48 inches of Sea Level Rise and a 5 year Storm Return Period

Flood Estimate - 48in Sea Level Rise 20yr Storm (nyserda:flood_48_0020)

The estimated flood extent with 48 inches of Sea Level Rise and a 20 year Storm Return Period

Flood Estimate - 48in Sea Level Rise 100yr Storm (nyserda:flood_48_0100)

The estimated flood extent with 48 inches of Sea Level Rise and a 100 year Storm Return Period

Flood Estimate - 48in Sea Level Rise 500yr Storm (nyserda:flood_48_0500)

The estimated flood extent with 48 inches of Sea Level Rise and a 500 year Storm Return Period

Flood Estimate - 60in Sea Level Rise 0yr Storm (nyserda:flood_60_0000)

The estimated flood extent with 60 inches of Sea Level Rise and a 0 year Storm Return Period

Flood Estimate - 60in Sea Level Rise 10yr Storm (nyserda:flood_60_0010)

The estimated flood extent with 60 inches of Sea Level Rise and a 10 year Storm Return Period

Flood Estimate - 60in Sea Level Rise 50yr Storm (nyserda:flood_60_0050)

The estimated flood extent with 60 inches of Sea Level Rise and a 50 year Storm Return Period

Flood Estimate - 60in Sea Level Rise 200yr Storm (nyserda:flood_60_0200)

The estimated flood extent with 60 inches of Sea Level Rise and a 200 year Storm Return Period

Flood Estimate - 60in Sea Level Rise 1000yr Storm (nyserda:flood_60_1000)

The estimated flood extent with 60 inches of Sea Level Rise and a 1000 year Storm Return Period

Flood Estimate - 72in Sea Level Rise 5yr Storm (nyserda:flood_72_0005)

The estimated flood extent with 72 inches of Sea Level Rise and a 5 year Storm Return Period

Flood Estimate - 72in Sea Level Rise 20yr Storm (nyserda:flood_72_0020)

The estimated flood extent with 72 inches of Sea Level Rise and a 20 year Storm Return Period

Flood Estimate - 72in Sea Level Rise 100yr Storm (nyserda:flood_72_0100)

The estimated flood extent with 72 inches of Sea Level Rise and a 100 year Storm Return Period

Flood Estimate - 72in Sea Level Rise 500yr Storm (nyserda:flood_72_0500)

The estimated flood extent with 72 inches of Sea Level Rise and a 500 year Storm Return Period

Forest Patches 2010 (nyserda:forest_patches_2010)

gcce-CO2-kt_2007 (gcce:gcce-CO2-kt_2007)

Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. Source Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United

NYC Office of Emergency Management (OEM) Hurricane Evacuation Zone 1 (gcce:gcce-OEM-hurricane-evacuation-zone_1)

OEM continues to educate the public about emergency preparedness. Programs like Ready New York, Community Emergency Response Teams, and Citizen Corps reach more New Yorkers every day through public outreach, volunteerism, and strategic partnerships.

Cereal Yield (kg/hectare) (gcce:gcce-cereal-yield)

Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. http://data.worldbank.org/indicator/AG.YLD.CREL.KG?display=default

Rice Production Average in Tons (gcce:gcce-crop-climate-production-rice)

Cyclone Events based on theSaffir Simpson Categories for 1969 to 2009 (gcce:gcce-cyclone-events)

Cyclone Physical Exposure 2010 (gcce:gcce-cyclone-phyexp)

Cyclone Surge Frequency (average per year 1975-2007) (gcce:gcce-cyclone-surge-frequency)

Cyclone Tracks (1969-2009) (gcce:gcce-cyclone-tracks)

This dataset includes a compilation Tropical cyclones best tracks 1969-2009. It is based on two sources: 1) A compilation of best tracks dataset from WMO Regional Specialised Meteorological Centres (RSMCs) and Tropical Cyclone Warning Centres (TCWCs). As well as personal communication with Dr. Varigonda Subrahmanyam, Dr. James Weyman, Kiichi Sasaki, Philippe CAROFF, Jim Davidson, Simon Mc Gree, Steve Ready, Peter Kreft, Henrike Brecht. 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: Compilation and GIS processing UNEP/GRID-Europe.

GDP per capita in 2009 in US Dollars (gcce:gcce-gdp-per-capita-2009)

Global Glacial Distribution (gcce:gcce-global-glacial-distribution)

test (afsis:gcce-kg-2051-75-A2)

The Koppen-Geiger climate classification map separates the world into five main climate types with many subtypes based on temperature and precipitation monthly averages as well as seasonality. The system relies on the concept that native vegetation indicates the climate of the region. World maps for the observational period 1901-2002 are based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service. World maps for the period 2003-2100 are based on ensemble projections of global climate models provided by the Tyndall Centre for Climate Change Research. This map specifically is projecting the climate classifications for 2051-75 with the IPCC's scenario A2, which predicts a regionally oriented world focused on economic development. Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z., 19, 135-141. DOI: 10.1127/0941-2948/2010/0430.

Koppen-Geiger 2001-2025 A1F1 (gcce:gcce-koppen-geiger_2001-2025_A1F1)

The Koppen-Geiger climate classification map separates the world into five main climate types with many subtypes based on temperature and precipitation monthly averages as well as seasonality. The system relies on the concept that native vegetation indicates the climate of the region. World maps for the observational period 1901-2002 are based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service. World maps for the period 2003-2100 are based on ensemble projections of global climate models provided by the Tyndall Centre for Climate Change Research. This map specifically is projecting the climate classifications for 2001-2025 with the IPCC's scenario A1F1, which predicts a globalized economically oriented world that receives its energy from fossil fuels. Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z., 19, 135-141. DOI: 10.1127/0941-2948/2010/0430.

Koppen-Geiger 2001-2025 B1 (gcce:gcce-koppen-geiger_2001-2025_B1)

The Koppen-Geiger climate classification map separates the world into five main climate types with many subtypes based on temperature and precipitation monthly averages as well as seasonality. The system relies on the concept that native vegetation indicates the climate of the region. World maps for the observational period 1901-2002 are based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service. World maps for the period 2003-2100 are based on ensemble projections of global climate models provided by the Tyndall Centre for Climate Change Research. This map specifically is projecting the climate classifications for 2001-25 with the IPCC's scenario B1, which predicts a globalized effort towards environmental sustainability. Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z., 19, 135-141. DOI: 10.1127/0941-2948/2010/0430.

Koppen-Geiger 2026-50 A1F1 (gcce:gcce-koppen-geiger_2026-2050_A1F1)

The Koppen-Geiger climate classification map separates the world into five main climate types with many subtypes based on temperature and precipitation monthly averages as well as seasonality. The system relies on the concept that native vegetation indicates the climate of the region. World maps for the observational period 1901-2002 are based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service. World maps for the period 2003-2100 are based on ensemble projections of global climate models provided by the Tyndall Centre for Climate Change Research. This map specifically is projecting the climate classifications for 2026-50 with the IPCC's scenario A1F1, which predicts a globalized economically oriented world that receives its energy from fossil fuels.

Koppen-Geiger 2026-50 B1 (gcce:gcce-koppen-geiger_2026-2050_B1)

The Koppen-Geiger climate classification map separates the world into five main climate types with many subtypes based on temperature and precipitation monthly averages as well as seasonality. The system relies on the concept that native vegetation indicates the climate of the region. World maps for the observational period 1901-2002 are based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service. World maps for the period 2003-2100 are based on ensemble projections of global climate models provided by the Tyndall Centre for Climate Change Research. This map specifically is projecting the climate classifications for 2026-50 with the IPCC's scenario B1, which predicts a globalized effort towards environmental sustainability. Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z., 19, 135-141. DOI: 10.1127/0941-2948/2010/0430.

Koppen-Geiger 2051-75-A1F1 (gcce:gcce-koppen-geiger_2051-2075_A1F1)

The Koppen-Geiger climate classification map separates the world into five main climate types with many subtypes based on temperature and precipitation monthly averages as well as seasonality. The system relies on the concept that native vegetation indicates the climate of the region. World maps for the observational period 1901-2002 are based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service. World maps for the period 2003-2100 are based on ensemble projections of global climate models provided by the Tyndall Centre for Climate Change Research. This map specifically is projecting the climate classifications for 2051-75 with the IPCC's scenario A1F1, which predicts a globalized economically oriented world that receives its energy from fossil fuels. Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z., 19, 135-141. DOI: 10.1127/0941-2948/2010/0430.

Koppen-Geiger 2051-75-B2 (gcce:gcce-koppen-geiger_2051-2075_B2)

The Koppen-Geiger climate classification map separates the world into five main climate types with many subtypes based on temperature and precipitation monthly averages as well as precipitation seasonality. The system relies on the concept that native vegetation indicates the climate of the region. World maps for the observational period 1901-2002 are based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service. World maps for the period 2003-2100 are based on ensemble projections of global climate models provided by the Tyndall Centre for Climate Change Research. This map specifically is projecting the climate classifications for 2051-2075 with the IPCC's scenario B2, which predicts greater local and regional efforts towards sustainability. Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z., 19, 135-141. DOI: 10.1127/0941-2948/2010/0430.

Koppen-Geiger 2076-2100 A2 (gcce:gcce-koppen-geiger_2076-2100_A2)

The Koppen-Geiger climate classification map separates the world into five main climate types with many subtypes based on temperature and precipitation monthly averages as well as seasonality. The system relies on the concept that native vegetation indicates the climate of the region. World maps for the observational period 1901-2002 are based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service. World maps for the period 2003-2100 are based on ensemble projections of global climate models provided by the Tyndall Centre for Climate Change Research. This map specifically is projecting the climate classifications for 2075-2100 with the IPCC's scenario A2, which predicts a regionally oriented world focused on economic development. Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z., 19, 135-141. DOI: 10.1127/0941-2948/2010/0430.

Koppen-Geiger 2076-2100 B2 (gcce:gcce-koppen-geiger_2076-2100_B2)

The Koppen-Geiger climate classification map separates the world into five main climate types with many subtypes based on temperature and precipitation monthly averages as well as seasonality. The system relies on the concept that native vegetation indicates the climate of the region. World maps for the observational period 1901-2002 are based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service. World maps for the period 2003-2100 are based on ensemble projections of global climate models provided by the Tyndall Centre for Climate Change Research. This map specifically is projecting the climate classifications for 2076-2100 with the IPCC's scenario B2, which predicts greater local and regional efforts towards sustainability. Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the K?ppen-Geiger climate classification. Meteorol. Z., 19, 135-141. DOI: 10.1127/0941-2948/2010/0430.

Nitrogen Fertilizer Application (kg/hectare) (gcce:gcce-nitrogen-fertilizer)

Data values derived by fusing global maps of harvest areas for 175 crops with national information on fertilizer use for each crop. Potter, P., N. Ramankutty, E. M. Bennett and S. D. Donner. 2010. Characterizing the spatial patterns of global fertilizer application and manure production. Earth Interactions 14(002):1-22. Data distributed by the Socioeconomic Data and Applications Center (SEDAC): http://sedac.ciesin.columbia.edu/data/collection/fertilizer-and-manure.html. [2011, July 13]

Precipitation Rate Change A2 2080s (gcce:gcce-precipitation-change-A2-2080s)

Precipitation Rate Change B1 2050s (gcce:gcce-precipitation-change_B1-2050s)

Cultivated Rain-fed Land (%) (gcce:gcce-rain-fed-cultivated-land)

Six geographic datasets were used for the compilation of an inventory of seven major land cover/land use categories at 5’ resolution. The datasets used are: 1. GLC2000 land cover database at 30 arc-sec (http://www-gvm.jrc.it/glc2000), using regional and global legends; 2. an IFPRI global land cover categorization providing 17 land cover classes at 30 arc-sec. (IFPRI, 2002), based on a reinterpretation of the Global Land Cover Characteristics Database (GLCC ver. 2.0), EROS Data Centre (EDC, 2000); 3. FAO’s Global Forest Resources Assessment 2000 (FAO, 2001) at 30 arc-sec. resolution; 4. digital Global Map of Irrigated Areas (GMIA) version 4.0 of (FAO/University of Frankfurt) at 5’ by 5’ latitude/longitude resolution, providing by grid-cell the percentage land area equipped with irrigation infrastructure; 5. IUCN-WCMC protected areas inventory at 30-arc-seconds (http://www.unep-wcmc.org/wdpa/index.htm), and 6. a spatial population density inventory (30-arc seconds) for year 2000 developed by FAO-SDRN, based on spatial data of LANDSCAN 2003, with calibration to UN 2000 population figures. An iterative calculation procedure has been implemented to estimate land cover class weights, consistent with aggregate FAO land statistics and spatial land cover patterns obtained from (the above mentioned) remotely sensed data, allowing the quantification of major land use/land cover shares in individual 5’ by 5’ latitude/longitude grid cells. The estimated class weights define for each land cover class the presence of respectively cultivated land and forest. Starting values of class weights used in the iterative procedure were obtained by cross-country regression of statistical data of cultivated and forest land against land cover class distributions obtained from GIS, aggregated to national level. The percentage of urban/built-up land in a grid-cell was estimated based on presence of respective land cover classes as well as regression equations relating built-up land with number of people and population density. Remaining areas were allocated to: 1. grassland and other vegetated areas (excluding cultivated land and forest); 2. barren or very sparsely vegetated areas, and 3. water bodies according to indicated land cover classes. Barren or very sparsely vegetated areas (class (ii) above) were delineated from (i) using the respective land cover information in GLC 2000 and a minimum bio-productivity threshold. The resulting seven land use land cover categories shares are: 1. Rain-fed cultivated land; 2. Irrigated cultivated land; 3. Forest; 4. Pastures and other vegetated land; 5. Barren and very sparsely vegetated land; 6. Water; and 7. Urban land and land required for housing and infrastructure. Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy. http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/CULTRF_2000.html

2 Meter Sea Level Rise (gcce:gcce-sea-level-rise-2-meter)

4 Meter Sea Level Rise (gcce:gcce-sea-level-rise-4-meter)

6 Meter Sea Level Rise (gcce:gcce-sea-level-rise-6-meter)

Soil Nutrient Retention Capability (gcce:gcce-soil-nutrient-retention)

Soil Organic carbon, Soil texture, base saturation, cation exchange capacity of soil and of clay fraction. Nutrient retention capacity is of particular importance for the effectiveness of fertilizer applications and is therefore of special relevance for intermediate and high input level cropping conditions. Nutrient retention capacity refers to the capacity of the soil to retain added nutrients against losses caused by leaching. Plant nutrients are held in the soil on the exchange sites provided by the clay fraction, organic matter and the clay-humus complex. Losses vary with the intensity of leaching which is determined by the rate of drainage of soil moisture through the soil profile. Soil texture affects nutrient retention capacity in two ways, through its effects on available exchange sites on the clay minerals and by soil permeability. Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.

Soil Salinity (gcce:gcce-soil-salinity)

Soil salinity, soil sodicity and soil phases influencing salt conditions. Accumulation of salts may cause salinity. Excess of free salts referred to as soil salinity is measured as Electric Conductivity (EC in dS/m) or as saturation of the exchange complex with sodium ions, which is referred to as sodicity or sodium alkalinity and is measured as Exchangeable Sodium Percentage (ESP). Salinity affects crops through inhibiting the uptake of water. Moderate salinity affects growth and reduces yields; high salinity levels may kill the crop. Sodicity causes sodium toxicity and affects soil structure leading to massive or coarse columnar structure with low permeability. Apart from soil salinity and sodicity, conditions indicated by saline (salic) and sodic soil phases may affect crop growth and yields. In case of simultaneous occurrence of saline (salic) and sodic soils the limitations are combined. The most limiting of the combined soil salinity and/or sodicity conditions and occurrence of saline (salic) and/or sodic soil phase is selected. http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/SQ5.html FAO/IIASA/ISRIC/ISSCAS/JRC, 2009. Harmonized World Soil Database (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria.

Soil Workability (gcce:gcce-soil-workability)

Soil texture, effective soil depth/volume, and soil phases constraining soil management (soil depth, rock outcrop, stoniness, gravel/concretions and hardpans) Diagnostic characteristics to indicate soil workability vary by type of management applied. Workability or ease of tillage depends on interrelated soil characteristics such as texture, structure, organic matter content, soil consistence/bulk density, the occurrence of gravel or stones in the profile or at the soil surface, and the presence of continuous hard rock at shallow depth as well as rock outcrops. Some soils are easy to work independent of moisture conditions, other soils are only manageable at an adequate moisture status, in particular for manual cultivation or light machinery. Irregular soil depth, gravel and stones in the profile and rock outcrops, might prevent the use of heavy farm machinery. The soil constraints related to soil texture and soil structure are particularly affecting low and intermediate input farming LUTs, while the constraints related to irregular soil depth and stony and rocky soil conditions are foremost affecting mechanized land preparation and harvesting operations, of high-level input mechanized farming LUTs. Workability constraints are therefore handled differently for low/intermediate and high inputs. Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy. http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/SQ7.html

Sea Surface Temperature Cyclone Nargis (gcce:gcce-sst-cyclone_nargis-2008)

This layer shows the sea surface temperature in Celsius during the time period of April 27-May 3, 2008. Reynolds, R.W., N.A. Rayner, T.M. Smith, D.C. Stokes, and W. Wang, 2002: An Improved In Situ and Satellite SST Analysis for Climate. J. Climate, 15, 1609-1625.

Hurricane Irene scenario storm track 2011 (gcce:gcce-storm-track-scenario_2011)

Ghana Migration from Takpo (care:gha-takpo-flow)

Ghana Study Villages (care:gha-villages)

grande_anse_mask (haitiegov:grande-anse-mask)

Guatemala Migration from El Cerro (care:gtm-el-cerro-flow)

Guatemala Migration from Quiquibaj (care:gtm-quiquibaj-flow)

Guatemala Source Villages (care:gtm-villages)

Heliports (nyserda:heliports)

A point file of selected heliport locations in New York State.

Tidal Wetlands 2007 (nyserda:hre_tidal_wetlands_2007)

This is the second tidal wetlands inventory covering this area of the Hudson. The first was undertaken by NYS DEC in 1998 and produced the HR Estuary Wetland Mapper (Mushacke, 1998). An earlier inventory of tidal wetlands, also performed by NYS DEC, focused on areas south of the Tappan Zee Bridge although it did extend northward enough to include HRNERR's Piermont Marsh (NYSDEC, 1974). NYS DEC Freshwater wetlands data also covers the study area.

Etablissement de sante Infrastructure (haiti:hti-infrastructure-healthfacility)

Haiti Health Facilities

Les zones de subsistance (haiti:hti-livelihood-zones)

Haiti Livelihood Zones

Limite Section-Communale (haiti:hti-section-communale)

Haiti Administrative Level 3

Important Areas - Animal (nyserda:important_areas_animal)

India Migration from Akalteri (care:ind-akalteri-flow)

India Migration from Julian Pakaria (care:ind-julian-pakaria-flow)

India Unique Points (care:ind-unique-points)

Jamaica Bay Water Quality: Area Data View (jbwq:jbwq-area-data-view)

Jamaica Bay Water Quality: Filter Data View (jbwq:jbwq-filter-data-view)

Jamaica Bay Water Quality: Location Data View (jbwq:jbwq-loc-data-view)

Jamaica Bay Water Quality: Off Areas View (jbwq:jbwq-off-areas-view)

Jamaica Bay Water Quality: Sample Data (jbwq:jbwq-sample-data)

Jamaica Bay Water Quality: Sampling Areas View (jbwq:jbwq-sampling-area-view)

Jamaica Bay Water Quality: Sampling Locations (jbwq:jbwq-sampling-location)

Jamaica Bay Water Quality: Sampling Locations View (jbwq:jbwq-sampling-location-view)

Jamaica Bay Water Quality: Site Locations (jbwq:jbwq-site-locations)

Jamaica Bay Water Quality: Site Locations represents all collection sites for National Park Service (NPS) and New York City Department of Environmental Protection (NYCDEP). Data created by Brooklyn College and CIESIN @ Columbia University December, 2014.

Linear hydrography (nyserda:linear_hydrography)

This data feature includes creek river tributary hollow brook or canal in New York State. The data was obtained from NYS GIS Clearinghouse https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=932 in January 2014.

Important Areas for Significant Natural Communities (nyserda:natural_communities)

Natural Communities - Palustrine (nyserda:natural_communities_palustrine)

Natural Communities - Terrestrial (nyserda:natural_communities_terrestrial)

nippes_mask (haitiegov:nippes-mask)

nord_mask (haitiegov:nord-mask)

Nursing homes (nyserda:nursing_homes)

The Nursing Homes Layer was split into two sub-layers. The Nursing Homes Location data set displays the location of Nursing Homes within the study area. The data was obtained from NYS GIS Clearinghouse at http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=929 in May 2013 and April 2014 . The Hospitals Number of Beds data set shows the different locations of the hospitals within the study area as well as the amount of people that can reside in the building. The data was obtained from NYS GIS Clearinghouse at http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=929 in January 2014 and additional data was obtained from the New York State Department of Health at http://hospitals.nyhealth.gov/ in January 2014.

ouest_mask (haitiegov:ouest-mask)

Peru Migration From Chamiseria (care:per-chamiseria-flow)

Peru Unique Points (care:per-unique-points)

Port-a-Piment du Bassin Versant (haiti:pim-watershed)

Port-a-Piment Watershed

Police stations (nyserda:police_stations)

The Emergency Services data set describes the different types and locations of emergency services within the study area. The data was created using Google Maps at https://www.google.com/maps and was obtained in April 2014.

Prisons (nyserda:prisons)

The Prisons layer was divided into two sub-layers. The Prison Security data set displays the locations of prisons within the study area. The data was obtained from the Correctional Association of New York the New York State Department of Corrections and Community Supervision the Federal Bureau of Prisons and LoHud (Journal News) at http://www.doccs.ny.gov/faclist.html ; http://www.correctionalassociation.org/wp-content/uploads/2012/05/bedford_2007.pdf ; http://www.correctionalassociation.org/resource/fishkill-correctional-facility-2 http://www.correctionalassociation.org/resource/coxsackie-correctional-facility http://www.bop.gov/locations/institutions/otv/ http://www.correctionalassociation.org/resource/hudson-correctional-facility-reentry-unit http://www.correctionalassociation.org/resource/sing-sing-correctional-facility http://www.correctionalassociation.org/resource/downstate-correctional-facility http://www.correctionalassociation.org/resource/wallkill-correctional-facility http://www.correctionalassociation.org/resource/shawangunk-correctional-facility http://www.correctionalassociation.org/resource/greene-correctional-facility http://www.lohud.com/article/99999999/WATCHDOG/399990118/N-Y-has-excess-prison-beds-staff-review-shows http://abolishcontrolunits.org/research/NY in January 2014. The Prison Security data set displays the classification of the prisons as either a minimum or maximum secutiry prison. The data was obtained from the Correctional Association of New York the New York State Department of Corrections and Community Supervision the Federal Bureau of Prisons and LoHud (Journal News) at http://www.doccs.ny.gov/faclist.html ; http://www.correctionalassociation.org/wp-content/uploads/2012/05/bedford_2007.pdf ; http://www.correctionalassociation.org/resource/fishkill-correctional-facility-2 http://www.correctionalassociation.org/resource/coxsackie-correctional-facility http://www.bop.gov/locations/institutions/otv/ http://www.correctionalassociation.org/resource/hudson-correctional-facility-reentry-unit http://www.correctionalassociation.org/resource/sing-sing-correctional-facility http://www.correctionalassociation.org/resource/downstate-correctional-facility http://www.correctionalassociation.org/resource/wallkill-correctional-facility http://www.correctionalassociation.org/resource/shawangunk-correctional-facility http://www.correctionalassociation.org/resource/greene-correctional-facility http://www.lohud.com/article/99999999/WATCHDOG/399990118/N-Y-has-excess-prison-beds-staff-review-shows http://abolishcontrolunits.org/research/NY in January 2014.

Railroad junctions (nyserda:railroad_junctions)

The Railroad Junction Location data set displays the locations of railroad junctions within the study area. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1102 in April 2014.

Railroads (nyserda:railroads)

The Railroads layer was divided into five sub-layers. The Railroad Location data set displays the locations of all railroad lines in the study area. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=904 in April 2014. The Electric Railroads data set displays which (if any) rails on the railroad track are electrified. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=904 in April 2014. The Railroad Subtype data set displays the different types of railroads and what they are used for. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=904 in April 2014. The Railroad Parent Company data set displays the different owners and users of the railroad lines in the study area. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=904 in April 2014. The Railroad Type data set displays the different types of railroad lines and what purpose they serve within the study area. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=904 in April 2014.

JBWQ Bounding Area (200m buffer) (jbwq:sample-envelope-200m)

Social Vulnerability Index (nyserda:sovi)

The Social Vulnerability Index (SOVI) data set includes the SOVI scores and five different principle component analysis results at New York State's block. The data set was generated by Center for International Earth Science Information Network at Columbia University.

SPDES Wastewater (nyserda:spdes_wastewater)

The SPDES Wastewater data set displays the locations of wastewater treatment facilities that discharge to the groundwaters as well as surface waters of New York State. The facilities are classified as munipal, industrial, private, commercial, or institutional. The data was obtained from New York State at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1010 in April 2014.

sud_mask (haitiegov:sud-mask)

Thailand Migration from Huai Ping (care:tha-huai-ping-flow)

Thailand Study Villages (care:tha-villages)

Tanzania Migration From Ruvu Mferejini (care:tza-ruvu-flow)

Tanzania Study Villages (care:tza-villages)

Thailand Migration from Hung Thanh (care:vnm-hung-thanh-flow)

Vietnam Study Villages (care:vnm-villages)

Withdrawal Location (nyserda:water_withdrawal_location)

Data regarding water withdrawals has been collected since 1990. Data is currently collected in accordance with the requirements of Environmental Conservation Law (ECL) 15-1501 that requires a DEC permit and annual usage reporting for all facilities using water for any purpose and having the capacity to withdraw 100,000 gallons or more per day of surface or groundwater.