CIESIN, Columbia University

afsis:afsis Area Equipped for Irrigation afsis:afsis Area Equipped for Irrigation afsis:afsis Area Equipped for Irrigation
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Web Service, OGC Web Map Service 1.3.0
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WFS, WMS, GEOSERVER
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CIESIN, Columbia University (unverified)

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CIESIN, Columbia University

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Alexandria, USA

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A compliant implementation of WMS 1.1.1 plus most of the SLD 1.0 extension (dynamic styling). Can also generate PDF, SVG, KML, GeoRSS

Available map layers (468)

hillshade_dem (hillshade_dem)

Layer-Group type layer: hillshade_dem

hillshade_sca (hillshade_sca)

Layer-Group type layer: hillshade_sca

hillshade_twi (hillshade_twi)

Layer-Group type layer: hillshade_twi

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

NIFPILOT data for mapping 9/13/2013 (haitiegov:NIFIPILOT-fixed)

Adaptation Areas (nyserda:adaptation_areas)

1st Std. Depth 0-5 CM (afsis:afsis-BLD-1km-sd1-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 first standard depth (0-5 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-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

3rd Std. Depth 15-30 CM (afsis:afsis-BLD-1km-sd3-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 third standard depth (15-30 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

5th Std. Depth 60-100 CM (afsis:afsis-BLD-1km-sd5-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 fifth standard depth (60-100 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

1st Std. Depth 0-5 CM (afsis:afsis-CEC-1km-sd1-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 first standard depth (0-5 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

3rd Std. Depth 15-30 CM (afsis:afsis-CEC-1km-sd3-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 third standard depth (15-30 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

5th Std. Depth 60-100 CM (afsis:afsis-CEC-1km-sd5-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 fifth standard depth (60-100 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

1st Std. Depth 0-5 CM (afsis:afsis-CLYPPT-1km-sd1-M)

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

3rd Std. Depth 15-30 CM (afsis:afsis-CLYPPT-1km-sd3-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 third standard depth (15-30 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

5th Std. Depth 60-100 CM (afsis:afsis-CLYPPT-1km-sd5-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 fifth standard depth (60-100 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

1st Std. Depth 0-5 CM (afsis:afsis-ORCDRC-1km-sd1-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 first standard depth (0-5 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

3rd Std. Depth 15-30 CM (afsis:afsis-ORCDRC-1km-sd3-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 third standard depth (15-30 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

5th Std. Depth 60-100 CM (afsis:afsis-ORCDRC-1km-sd5-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 fifth standard depth (60-100 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

1st Std. Depth 0-5 CM (afsis:afsis-PHIHO5-1km-sd1-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 first standard depth (0-5 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

3rd Std. Depth 15-30 CM (afsis:afsis-PHIHO5-1km-sd3-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 third standard depth (15-30). 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

5th Std. Depth 60-100 CM (afsis:afsis-PHIHO5-1km-sd5-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 fifth standard depth (60-100 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

1st Std. Depth 0-5 CM (afsis:afsis-SLTPPT-1km-sd1-M)

his 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 first standard depth (0-5 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

3rd Std. Depth 15-30 CM (afsis:afsis-SLTPPT-1km-sd3-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 third standard depth (15-30 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

5th Std. Depth 60-100 CM (afsis:afsis-SLTPPT-1km-sd5-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 fifth standard depth (60-100 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

1st Std. Depth 0-5 CM (afsis:afsis-SNDPPT-1km-sd1-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 first standard depth (0-5 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

3rd Std. Depth 15-30 CM (afsis:afsis-SNDPPT-1km-sd3-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 third standard depth (15-30 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

5th Std. Depth 60-100 CM (afsis:afsis-SNDPPT-1km-sd5-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 fifth standard depth (60-100 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

TRMM Precipitation (afsis:afsis-TRMM-average)

This directory contains the Africa continent-wide rasters of the annual average and long term average of precipitation 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.

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: BIO1 Temperature Long Term Average (Celsius) (afsis:afsis-WorldClim-BIO1-annual-mean-temperature)

The Africa continent-wide BIO1 Temperature Long Term 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.

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.

WorldClim: BIO12 Precipitation Modified Fournier Index Long Term (afsis:afsis-WorldClim-BIO12-modified-fournier-index)

The Africa continent-wide BIO12 Modified Fournier Index Long Term Precipitation 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. Total precipitation is squared for each month. The sum of the squared monthly precipitation is divided by the total precipitation

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

Annual Mean Temperature (Celsius) (afsis:afsis-annual-mean-temperature)

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

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

Blue Reflectance (afsis:afsis-blue-reflectance)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Blue Reflectance (Band 3) 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.

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

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

Ecoregions (afsis:afsis-ecoregions)

Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V.N., Underwood, E.C., D'Amico, J.A., Itaou, I., Strand, H.E., Morisson, J.C., Loucks, C.J., Allnutt, T.F., Ricketts, T.H., Kura, Y., Lamoreux, J.F., Wettengel, W.W., Hedao, P. and K.R. Kassem. 2001. Terrestrial Ecoregions of the World: A New Map of Life on Earth. Bioscience 51, 933-938.

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.

FAO Soil groups (afsis:afsis-fao-soil-groups)

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.fao.org/nr/water/art/2008/soil_map2.html

Field Locations (afsis:afsis-field-locations)

Fraction of Photosynthetically Active Radiation (%) (afsis:afsis-fraction-of-photosynthetically-active-radiation)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Fraction of Photosynthetically Active Radiation (FPAR) 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

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

Gross Primary Production (kg C/m2) (afsis:afsis-gross-primary-production)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: AfSIS MODIS Data Sets: Gross Primary Production (GPP) Annual and Long-term Averages, 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

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, Day (Celsius) (afsis:afsis-land-surface-temperature-day)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Land Surface Temperature (LST) Day 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.

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.

Landcover (UMD Type 2) (afsis:afsis-landcover-type-2)

The recommended citation for this data is: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Land Cover Type 2 (UMD) Annual Averages. 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

Maximum Temperature Warmest Month (Celsius) (afsis:afsis-max-temperature-warmest-month)

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

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.

National Boundaries (afsis:afsis-national-boundaries)

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.

Net Primary Production (kg C/m2) (afsis:afsis-net-primary-production)

The recommended citation for this data is: Please use this citation: Africa Soil Information Service (AfSIS). 2012. AfSIS MODIS Data Sets: Net Primary Production (NPP) Annual and Long-term Averages, 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

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.

Project Area (afsis:afsis-project-area)

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.

Rivers (afsis:afsis-rivers)

Sentinel Sites (afsis:afsis-sentinel-landscapes)

Site Boundaries (afsis:afsis-site-boundaries)

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

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

Topographic Wetness Index (TWI)-90m (afsis:afsis-topographic-wetness-index)

Shuttle RADAR Topographic Mission (SRTM) derivative: Topographic Wetness Index (TWI)

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.

Watersheds (afsis:afsis-watersheds)

Dissolved watershed layers for Africa

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.

Area hydrography (nyserda:area_hydrography)

This is a vector file of area hydrography features such as lakes ponds reservoirs major rivers streams canals etc. in New York State. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=932 in January 2014.

artibonite_mask (haitiegov:artibonite-mask)

Bangladesh Migration from Holokhana (care:bgd-khamar-holokhana-flow)

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

Bangladesh Unique Points (care:bgd-unique-points)

Bangladesh Study Villages (care:bgd-villages)

Boat launches (nyserda:boat_launches)

The Boat lanches data set displays the locations of all of the boat lanches. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=932 in January 2014.

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 routes (nyserda:bus_routes)

The bus routes layer was divided into two sublayers. 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 . The Bus Route Bus Company data set displays the different bus companies that run on the bus lines in the study area. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1116 in April 2014.

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.

Banana (care:care-banana-binary)

Bean (care:care-bean-binary)

Cassava (care:care-cassava-binary)

Cowpeas (care:care-cowpeas-binary)

Drought Coefficient of Variation (care:care-drought-coefficient-of-variation)

Groundnut (care:care-groundnut-binary)

Infant Mortality Rates 2008 (care:care-imr-2008)

Jute (care:care-jute-binary)

Forest Land in 2000 (care:care-land-cover-forest-2000)

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

Rain Fed Cultivated Land in 2000 (care:care-land-cover-rainfed-2000)

Landslide Risk (care:care-landslide-risk)

Maize (care:care-maize-binary)

Millet (care:care-millet-binary)

Mustard (care:care-mustard-binary)

Pea (care:care-pea-binary)

Pigeonpea (care:care-pigeonpea-binary)

Potato (care:care-potato-binary)

Poverty Headcount in Africa (IFPRI) (care:care-poverty125and200-percent)

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

Rice (care:care-rice-binary)

Sesame (care:care-sesame-binary)

Sisal (care:care-sisal-binary)

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

Sorghum (care:care-sorghum-binary)

Soybean (care:care-soybean-binary)

Sunflower (care:care-sunflower-binary)

Sweet potato (care:care-sweetpotato-binary)

Wheat (care:care-wheat-binary)

Yam (care:care-yam-binary)

centre_mask (haitiegov:centre-mask)

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

Average Daily Minimum Temperature 1901 to 2002 (climate1stop:climate1stop-average-daily-minimum-temperature-1901-2002)

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

Average Number of Wet Days 1901 to 2002 (climate1stop:climate1stop-average-number-of-wet-days-1901-2002)

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

Frost Day Frequency 1901 to 2002 (climate1stop:climate1stop-frost-day-frequency-1901-2002)

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

Potential Evapotranspiration 2051 to 2060 Dry Model (climate1stop:climate1stop-potential-evapotranspiration-dry-model-2051-2060)

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

Potential Evapotranspiration 2051 to 2060 Mid Model (climate1stop:climate1stop-potential-evapotranspiration-mid-model-2051-2060)

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

Potential Evapotranspiration 2051 to 2060 Wet Model (climate1stop:climate1stop-potential-evapotranspiration-wet-model-2051-2060)

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

Precipitation 2031 to 2040 Dry Model (climate1stop:climate1stop-precipitation-dry-model-2031-2040)

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

Precipitation 2031 to 2040 Mid Model (climate1stop:climate1stop-precipitation-mid-model-2031-2040)

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

Precipitation 2031 to 2040 Wet Model (climate1stop:climate1stop-precipitation-wet-model-2031-2040)

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

Precipitation Yearly Average 1961 to 1990 (climate1stop:climate1stop-precipitation-yearly-average-1961-1990)

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

Runoff 2051 to 2060 Dry Model (climate1stop:climate1stop-runoff-dry-model-2051-2060)

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

Runoff 2051 to 2060 Mid Model (climate1stop:climate1stop-runoff-mid-model-2051-2060)

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

Runoff 2051 to 2060 Wet Model (climate1stop:climate1stop-runoff-wet-model-2051-2060)

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

Soil Moisture 2031 to 2040 Dry Model (climate1stop:climate1stop-soil-moisture-dry-model-2031-2040)

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

Soil Moisture 2031 to 2040 Mid Model (climate1stop:climate1stop-soil-moisture-mid-model-2031-2040)

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

Soil Moisture 2031 to 2040 Wet Model (climate1stop:climate1stop-soil-moisture-wet-model-2031-2040)

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

Soil Moisture Yearly Average 1961 to 1990 (climate1stop:climate1stop-soil-moisture-yearly-average-1961-1990)

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

Temperature 2051 to 2060 Dry Model (climate1stop:climate1stop-temperature-dry-model-2051-2060)

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

Temperature 2051 to 2060 Mid Model (climate1stop:climate1stop-temperature-mid-model-2051-2060)

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

Temperature 2051 to 2060 Wet Model (climate1stop:climate1stop-temperature-wet-model-2051-2060)

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

Counties (nyserda:counties)

The Counties data set is a vector file of ten counties' boundaries in New York State.

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

Mask Countries Surrounding Haiti (haitiegov:country-esri101-haiti-mask-small)

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

Household Indicators: Agriculture

Indicateurs des menages: Education (haiti:csi-hh-indicators-education)

Household Indicators: Education

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

Household Indicators: Energy

Indicateurs des menages: Environmental Risk (haiti:csi-hh-indicators-envrisk)

Household Indicators: Environmental Risk

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

Household Indicators: Food Security

Indicateurs des menages: Sexe (haiti:csi-hh-indicators-gender)

Household Indicators:Gender

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

Household Indicators: Sante

Indicateurs des menages: Infrastructure (haiti:csi-hh-indicators-infrastructure)

Household Indicators: Infrastructure

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

Household Indicators: Nutrition

Indicateurs des menages: Laver (haiti:csi-hh-indicators-wash)

Household Indicators: Wash

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

Haiti Population Density 2003

Etablissements (haiti:csi-settlements)

CSI Settlements

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.

DEC roads and trails (nyserda:dec_roads_trails)

This data provide a digital representation of transportation corridors on New York State DEC land. Line data locating and differentiating transportation corridors on state DEC lands. The data was obtained from New York State Department of Environmental Conservation at http://www.dec.ny.gov/.

egov-sample (haitiegov:egov-sample)

EIA power plants (nyserda:eia_power_plants)

This is a point dataset representing operable electric generating plants in the United States by energy source. This includes plants that are operating or short- or long-term out of service. The data was obtained from http://www.eia.gov/.

Emergency operations centers (nyserda:emergency_operations_centers)

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

EMS (nyserda:ems)

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.

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

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

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

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

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

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

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

Buildings - Condo Main (fib:fib-buildings-condo-main)

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

Buildings - Industrial (fib:fib-buildings-industrial)

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

Buildings - Residential Lot (fib:fib-buildings-r-lot)

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

Buildings - Residential 2 - 6 Units (fib:fib-buildings-r2-r6)

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

Bus Routes (fib:fib-bus-routes)

Bus Stops (fib:fib-bus-stops)

Community Health Care Facilities (fib:fib-community-health-care)

Corner Stores (fib:fib-corner-stores)

Farmers Markets (fib:fib-farmers)

Grocery Stores (fib:fib-grocery)

Historic Districts (fib:fib-historic-districts)

Hospitals (fib:fib-hospitals-extent)

Landmarks (fib:fib-landmarks)

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

Parks and Playgrounds (fib:fib-parks-playgrounds)

Flood Pathways (fib:fib-pathways)

Pharmacies (fib:fib-pharmacies-point)

Public Housing (fib:fib-public-housing)

Public Schools (fib:fib-public-schools)

Roads (fib:fib-roads)

Study Area Boundary (fib:fib-study-area-boundary)

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

Train Lines (fib:fib-trains-arc)

Train Stations (fib:fib-trains-stations)

Fire stations (nyserda:fire_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.

Flood Scenario (nyserda:flood_00_0005)

No Sea Level Rise, 5 Year Storm Return Period

Flood Scenario (nyserda:flood_00_0010)

No Sea Level Rise, 10 Year Storm Return Period

Flood Scenario (nyserda:flood_00_0020)

No Sea Level Rise, 20 Year Storm Return Period

Flood Scenario (nyserda:flood_00_0050)

No Sea Level Rise, 50 Year Storm Return Period

Flood Scenario (nyserda:flood_00_0100)

No Sea Level Rise, 100 Year Storm Return Period

Flood Scenario (nyserda:flood_00_0200)

No Sea Level Rise, 200 Year Storm Return Period

Flood Scenario (nyserda:flood_00_0500)

No Sea Level Rise, 500 Year Storm Return Period

Flood Scenario (nyserda:flood_00_1000)

No Sea Level Rise, 1000 Year Storm Return Period

Flood Scenario (nyserda:flood_06_0005)

6 Inch Sea Level Rise, 5 Year Storm Return Period

Flood Scenario (nyserda:flood_06_0010)

6 Inch Sea Level Rise, 10 Year Storm Return Period

Flood Scenario (nyserda:flood_06_0020)

6 Inch Sea Level Rise, 20 Year Storm Return Period

Flood Scenario (nyserda:flood_06_0050)

6 Inch Sea Level Rise, 50 Year Storm Return Period

Flood Scenario (nyserda:flood_06_0100)

6 Inch Sea Level Rise, 100 Year Storm Return Period

Flood Scenario (nyserda:flood_06_0200)

6 Inch Sea Level Rise, 200 Year Storm Return Period

Flood Scenario (nyserda:flood_06_0500)

6 Inch Sea Level Rise, 500 Year Storm Return Period

Flood Scenario (nyserda:flood_06_1000)

6 Inch Sea Level Rise, 1000 Year Storm Return Period

Flood Scenario (nyserda:flood_12_0005)

12 Inch Sea Level Rise, 5 Year Storm Return Period

Flood Scenario (nyserda:flood_12_0010)

12 Inch Sea Level Rise, 10 Year Storm Return Period

Flood Scenario (nyserda:flood_12_0020)

12 Inch Sea Level Rise, 20 Year Storm Return Period

Flood Scenario (nyserda:flood_12_0050)

12 Inch Sea Level Rise, 50 Year Storm Return Period

Flood Scenario (nyserda:flood_12_0100)

12 Inch Sea Level Rise, 100 Year Storm Return Period

Flood Scenario (nyserda:flood_12_0200)

12 Inch Sea Level Rise, 200 Year Storm Return Period

Flood Scenario (nyserda:flood_12_0500)

12 Inch Sea Level Rise, 500 Year Storm Return Period

Flood Scenario (nyserda:flood_12_1000)

12 Inch Sea Level Rise, 1000 Year Storm Return Period

Flood Scenario (nyserda:flood_18_0005)

18 Inch Sea Level Rise, 5 Year Storm Return Period

Flood Scenario (nyserda:flood_18_0010)

18 Inch Sea Level Rise, 10 Year Storm Return Period

Flood Scenario (nyserda:flood_18_0020)

18 Inch Sea Level Rise, 20 Year Storm Return Period

Flood Scenario (nyserda:flood_18_0050)

18 Inch Sea Level Rise, 50 Year Storm Return Period

Flood Scenario (nyserda:flood_18_0100)

18 Inch Sea Level Rise, 100 Year Storm Return Period

Flood Scenario (nyserda:flood_18_0200)

18 Inch Sea Level Rise, 200 Year Storm Return Period

Flood Scenario (nyserda:flood_18_0500)

18 Inch Sea Level Rise, 500 Year Storm Return Period

Flood Scenario (nyserda:flood_18_1000)

18 Inch Sea Level Rise, 1000 Year Storm Return Period

Flood Scenario (nyserda:flood_24_0005)

24 Inch Sea Level Rise, 5 Year Storm Return Period

Flood Scenario (nyserda:flood_24_0010)

24 Inch Sea Level Rise, 10 Year Storm Return Period

Flood Scenario (nyserda:flood_24_0020)

24 Inch Sea Level Rise, 20 Year Storm Return Period

Flood Scenario (nyserda:flood_24_0050)

24 Inch Sea Level Rise, 50 Year Storm Return Period

Flood Scenario (nyserda:flood_24_0100)

24 Inch Sea Level Rise, 100 Year Storm Return Period

Flood Scenario (nyserda:flood_24_0200)

24 Inch Sea Level Rise, 200 Year Storm Return Period

Flood Scenario (nyserda:flood_24_0500)

24 Inch Sea Level Rise, 500 Year Storm Return Period

Flood Scenario (nyserda:flood_24_1000)

24 Inch Sea Level Rise, 1000 Year Storm Return Period

Flood Scenario (nyserda:flood_30_0005)

30 Inch Sea Level Rise, 5 Year Storm Return Period

Flood Scenario (nyserda:flood_30_0010)

30 Inch Sea Level Rise, 10 Year Storm Return Period

Flood Scenario (nyserda:flood_30_0020)

30 Inch Sea Level Rise, 20 Year Storm Return Period

Flood Scenario (nyserda:flood_30_0050)

30 Inch Sea Level Rise, 50 Year Storm Return Period

Flood Scenario (nyserda:flood_30_0100)

30 Inch Sea Level Rise, 100 Year Storm Return Period

Flood Scenario (nyserda:flood_30_0200)

30 Inch Sea Level Rise, 200 Year Storm Return Period

Flood Scenario (nyserda:flood_30_0500)

30 Inch Sea Level Rise, 500 Year Storm Return Period

Flood Scenario (nyserda:flood_30_1000)

30 Inch Sea Level Rise, 1000 Year Storm Return Period

Flood Scenario (nyserda:flood_36_0005)

36 Inch Sea Level Rise, 5 Year Storm Return Period

Flood Scenario (nyserda:flood_36_0010)

36 Inch Sea Level Rise, 10 Year Storm Return Period

Flood Scenario (nyserda:flood_36_0020)

36 Inch Sea Level Rise, 20 Year Storm Return Period

Flood Scenario (nyserda:flood_36_0050)

36 Inch Sea Level Rise, 50 Year Storm Return Period

Flood Scenario (nyserda:flood_36_0100)

36 Inch Sea Level Rise, 100 Year Storm Return Period

Flood Scenario (nyserda:flood_36_0200)

36 Inch Sea Level Rise, 200 Year Storm Return Period

Flood Scenario (nyserda:flood_36_0500)

36 Inch Sea Level Rise, 500 Year Storm Return Period

Flood Scenario (nyserda:flood_36_1000)

36 Inch Sea Level Rise, 1000 Year Storm Return Period

Flood Scenario (nyserda:flood_48_0005)

48 Inch Sea Level Rise, 5 Year Storm Return Period

Flood Scenario (nyserda:flood_48_0010)

48 Inch Sea Level Rise, 10 Year Storm Return Period

Flood Scenario (nyserda:flood_48_0020)

48 Inch Sea Level Rise, 20 Year Storm Return Period

Flood Scenario (nyserda:flood_48_0050)

48 Inch Sea Level Rise, 50 Year Storm Return Period

Flood Scenario (nyserda:flood_48_0100)

48 Inch Sea Level Rise, 100 Year Storm Return Period

Flood Scenario (nyserda:flood_48_0200)

48 Inch Sea Level Rise, 200 Year Storm Return Period

Flood Scenario (nyserda:flood_48_0500)

48 Inch Sea Level Rise, 500 Year Storm Return Period

Flood Scenario (nyserda:flood_48_1000)

48 Inch Sea Level Rise, 1000 Year Storm Return Period

Flood Scenario (nyserda:flood_60_0005)

60 Inch Sea Level Rise, 5 Year Storm Return Period

Flood Scenario (nyserda:flood_60_0010)

60 Inch Sea Level Rise, 10 Year Storm Return Period

Flood Scenario (nyserda:flood_60_0020)

60 Inch Sea Level Rise, 20 Year Storm Return Period

Flood Scenario (nyserda:flood_60_0050)

60 Inch Sea Level Rise, 50 Year Storm Return Period

Flood Scenario (nyserda:flood_60_0100)

60 Inch Sea Level Rise, 100 Year Storm Return Period

Flood Scenario (nyserda:flood_60_0200)

60 Inch Sea Level Rise, 200 Year Storm Return Period

Flood Scenario (nyserda:flood_60_0500)

60 Inch Sea Level Rise, 500 Year Storm Return Period

Flood Scenario (nyserda:flood_60_1000)

60 Inch Sea Level Rise, 1000 Year Storm Return Period

Flood Scenario (nyserda:flood_72_0005)

72 Inch Sea Level Rise, 5 Year Storm Return Period

Flood Scenario (nyserda:flood_72_0010)

72 Inch Sea Level Rise, 10 Year Storm Return Period

Flood Scenario (nyserda:flood_72_0020)

72 Inch Sea Level Rise, 20 Year Storm Return Period

Flood Scenario (nyserda:flood_72_0050)

72 Inch Sea Level Rise, 50 Year Storm Return Period

Flood Scenario (nyserda:flood_72_0100)

72 Inch Sea Level Rise, 100 Year Storm Return Period

Flood Scenario (nyserda:flood_72_0200)

72 Inch Sea Level Rise, 200 Year Storm Return Period

Flood Scenario (nyserda:flood_72_0500)

72 Inch Sea Level Rise, 500 Year Storm Return Period

Flood Scenario (nyserda:flood_72_1000)

72 Inch Sea Level Rise, 1000 Year Storm Return Period

Forest Patches 2010 (nyserda:forest_patches_2010)

full_study_area (nyserda:full_study_area)

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

Carbon Dioxide Emissions 2007 (per capita) (gcce:gcce-CO2-per-capita_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 States.

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.

Available Water by Drainage Basin 1961 to 1990 (gcce:gcce-available-water-drainage-basin-1961-1990)

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

Maize Production Average in Tons (gcce:gcce-crop-climate-production-maize)

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

Wheat Production Average in Tons (gcce:gcce-crop-climate-production-wheat)

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

Cyclone Intensity (maximum Saffir-Simpson score 1970-2009 (gcce:gcce-cyclone-intensity)

This dataset includes an estimate of tropical cyclones windspeed buffers footprint. It is based on two sources: 1) IBTrACS v02r01 (1969 - 2008, http://www.ncdc.noaa.gov/oa/ibtracs/), year 2009 completed by online data from JMA, JTWC, UNISYS, Meteo France and data sent by Alan Sharp from the Australian Bureau of Meteorology. 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. Unit is estimated maximum Saffir-Simpson categories over the period 1970-2009. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: Raw data: IBTrACS, compilation and GIS processing UNEP/GRID-Europe.

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

Cyclone Risk (gcce:gcce-cyclone-risk)

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

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

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.

Dams (gcce:gcce-dams)

The Global Reservoir and Dam Database, Version 1 (Revision 01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data. Lehner, B., Reidy Liermann, C., Revenga, C., Vorosmarty, C., Fekete, B., Crouzet, P., Doll P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., Wisser, D.

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

Global Fresh Water Bodies (gcce:gcce-global-fresh-water-bodies)

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

Cultivated Irrigated Land (%) (gcce:gcce-irrigated-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/CULTIR_2000.html

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 Climate Classifications 1951-2000 (gcce:gcce-koppen-geiger_1951-2000)

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. This Koppen-Geiger Climate Classification map for the years 1951-2000 was derived from 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. Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel, 2006: World Map of the Köppen-Geiger climate classification updated. Meteorol. Z., 15, 259-263. DOI: 10.1127/0941-2948/2006/0130.

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 A2 (gcce:gcce-koppen-geiger_2001-2025_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 2001-25 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 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 2001-2025 B2 (gcce:gcce-koppen-geiger_2001-2025_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 2001-25 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 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 A2 (gcce:gcce-koppen-geiger_2026-2050_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 2026-50 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 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 2026-50 B2 (gcce:gcce-koppen-geiger_2026-2050_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 2026-50 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 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-B1 (gcce:gcce-koppen-geiger_2051-2075_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 2051-75 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-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 A1F1 (gcce:gcce-koppen-geiger_2076-2100_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 2076-2100 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 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 B1 (gcce:gcce-koppen-geiger_2076-2100_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 2076-2100 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 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.

Land Suitable for Rainfed Agriculture (gcce:gcce-land-suitable-rain-fed-agriculture-cereals)

Suitability of currently available land for rainfed production of cereals (high level of inputs). FAO & IIASA. 2006. "Mapping biophysical factors that influence agricultural production and rural vulnerability". by H. van Velhuizen et. al. Environmental and Natural Resources Series No. 11 Rome. http://www.fao.org/geonetwork/srv/en/graphover.show?id=14080&fname=Map_6_06.png&access=public

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 Anomaly (mm) April-June 2011 (gcce:gcce-precipitation-anomaly_2011-Apr-Jun)

This layer show the precipitation anomaly for April to June 2011. The base period is from 1979-2000. Janowiak, J. E. and P. Xie, 1999: CAMS_OPI: A Global Satellite-Rain Gauge Merged Product for Real-Time Precipitation Monitoring Applications. J. Climate, vol. 12, 3335-3342.

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

Precipitation Rate Change A2 2050s (gcce:gcce-precipitation-change_A2-2050s)

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

Precipitation Rate Change B1 2080s (gcce:gcce-precipitation-change_B1-2080s)

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

1 Meter Sea Level Rise (gcce:gcce-sea-level-rise-1-meter)

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

3 Meter Sea Level Rise (gcce:gcce-sea-level-rise-3-meter)

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

5 Meter Sea Level Rise (gcce:gcce-sea-level-rise-5-meter)

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

Soil Nutrient Availability (gcce:gcce-soil-nutrient-availability)

Soil texture, soil organic carbon, soil pH, total exchangeable bases. This soil quality is decisive for successful low level input farming and to some extent also for intermediate input levels. Diagnostics related to nutrient availability are manifold. Important soil characteristics of the topsoil (0-30 cm) are: Texture/Structure, Organic Carbon (OC), pH and Total Exchangeable Bases (TEB). For the subsoil (30-100 cm), the most important characteristics considered are: Texture/Structure, pH and TEB. The soil characteristics relevant to soil nutrient availability are to some extent correlated. For this reason, the most limiting soil characteristic is combined in the evaluation with the average of the remaining less limiting soil characteristics to represent soil quality SQ1. 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/SQ1.html

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 Rooting Conditions (gcce:gcce-soil-rooting-conditions)

Soil textures, bulk density, coarse fragments, vertical soil properties and soil phases affecting root penetration and soil depth and soil volume. Rooting conditions include effective soil depth (cm) and effective soil volume (vol. %) related to presence of gravel and stoniness. Rooting conditions may be affected by the presence of a soil phase either limiting the effective rooting depth or decreasing the effective volume accessible for root penetration. Rooting conditions address various relations between soil conditions of the rooting zone and crop growth. The following factors are considered in the evaluation: 1. Adequacy of foothold, i.e., sufficient soil depth for the crop for anchoring; 2. available soil volume and penetrability of the soil for roots to extract nutrients; 3. space for root and tuber crops for expansion and economic yield in the soil; and 4. absence of shrinking and swelling properties (vertical) affecting root and tuber crops. Soil depth/volume limitations affect root penetration and may constrain yield formation (roots and tubers). Relevant soil properties considered are: soil depth, soil texture/structure, vertic properties, gelic properties, petric properties and presence of coarse fragments. This soil quality is estimated by multiplying of the soil depth limitation with the most limiting soil or soil phase property 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 Toxicity (gcce:gcce-soil-toxicity)

Calcium carbonate and gypsum. Low pH leads to acidity related toxicities, e.g., aluminum, iron, manganese toxicities, and to various deficiencies, e.g., of phosphorus and molybdenum. Calcareous soils exhibit generally micronutrient deficiencies, for instance of iron, manganese, and zinc and in some cases toxicity of molybdenum. Gypsum strongly limits available soil moisture. Tolerance of crops to calcium carbonate and gypsum varies widely (FAO, 1990; Sys, 1993). Low pH and high calcium carbonate and gypsum are mutually exclusive. Acidity related toxicities such as aluminum toxicities and micro-nutrient deficiencies are accounted for respectively in SQ1, nutrient availability, and in SQ2, nutrient retention capacity. This soil quality SQ6 is therefore only including calcium carbonate and gypsum related toxicities. The most limiting of the combination of excess calcium carbonate and gypsum in the soil, and occurrence of petrocalcic and petrogypsic soil phases is selected for the quantification of SQ6. 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/SQ6.html

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 Anomaly August 2005 (gcce:gcce-sst-anomaly_2005_Aug)

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 actual storm track 2011 (gcce:gcce-storm-track-actual_2011)

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

Ghana Migration from Nanville (care:gha-nanville-flow)

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

Ghana Unique Points (care:gha-unique-points)

Ghana Study Villages (care:gha-villages)

Ghana Migration from Zupiiri (care:gha-zupiiri-flow)

grande_anse_mask (haitiegov:grande-anse-mask)

Guatemala Migration from Buena Vista (care:gtm-buena-vista-flow)

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

Guatemala Migration from El Durazno (care:gtm-el-durazno-flow)

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

Guatemala Unique Points (care:gtm-unique-points)

Guatemala Source Villages (care:gtm-villages)

haiti-mask-small (haitiegov:haiti-mask-small)

Heliports (nyserda:heliports)

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

Hospitals (nyserda:hospitals)

The Hospital layer was split into two sub-layers. The Hospitals Location data set displays the location of all of the hospitals in the study area. 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 . 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.

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.

Limite Commune (haiti:hti-commune)

Haiti Administrative Level 2

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

Haiti Health Facilities

Reseau Hydrographie (haiti:hti-inlandwaters-rivers)

Haiti Inland Water and Rivers

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

Haiti Livelihood Zones

Reseau Routier (haiti:hti-roads)

Haiti Roads from Minustah

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

Haiti Administrative Level 3

Hudson River Estuary 2009 (nyserda:hudson_river_estuary)

The layer is divided into two sub-layers. The Hudson River Estuary Shoreline data set is a combination of DEC shoreline information from the submerged aquatic vegetation (SAV) project covering the estuary north of Manhattan and NOAA shoreline data for the New York/New Jersey Harbor. The purpose of this shoreline file is to provide a base map for any maps that include the Hudson River Estuary. The data was obtained from NYS GIS Clearinghouse at http://gis.ny.gov/gisdata/inventories/details.cfm?dsid=1136. The Hudson River Estuary Shoreline Type contains a shoreline inventory for the tidal Hudson River shoreline from the Tappan Zee Bridge north to Troy. The data was obtained from NYS GIS Clearinghouse at http://gis.ny.gov/gisdata/inventories/details.cfm?dsid=1136.

Important Areas - Animal (nyserda:important_areas_animal)

Important Areas - Plant (nyserda:important_areas_plant)

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

India Migration from Banahil (care:ind-banahil-flow)

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

India Migration from Silli (care:ind-silli-flow)

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

India Study Villages (care:ind-villages)

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

Jamaica Bay Water Quality: Areas (no samples) View (jbwq:jbwq-areas-nosamples-view)

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

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

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

Jamaica Bay Water Quality: Locs (no samples) View (jbwq:jbwq-locs-nosamples-view)

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

Jamaica Bay Water Quality: Off Locations View (jbwq:jbwq-off-locations-view)

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

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

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

Jamaica Bay Water Quality: Areas (w Samples) View (jbwq:jbwq-sampling-areas-w-samples-view)

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

Jamaica Bay Water Quality: Locations (w Samples) View (jbwq:jbwq-sampling-location-data-view)

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

Jamaica Bay Water Quality: Locations (w Samples) View (jbwq:jbwq-sampling-locations-w-samples-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.

Large culverts (nyserda:large_culverts)

The file includes all large culverts that carry a state highway. Information on NYSDOT's large culvert 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 in April 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.

Municipalities (nyserda:municipalities)

The Municipalities data set is a vector file of 164 municipalities' boundaries in New York State.

Important Areas for Significant Natural Communities (nyserda:natural_communities)

Natural Communities - Estaurine (nyserda:natural_communities_estaurine)

Natural Communities - Palustrine (nyserda:natural_communities_palustrine)

Natural Communities - Riverine (nyserda:natural_communities_riverine)

Natural Communities - Terrestrial (nyserda:natural_communities_terrestrial)

nifipilot-data-for-mapping (haitiegov:nifipilot-data-for-mapping)

nippes_mask (haitiegov:nippes-mask)

nord_est_mask (haitiegov:nord-est-mask)

nord_mask (haitiegov:nord-mask)

nord_ouest_mask (haitiegov:nord-ouest-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.

NWI Wetlands (nyserda:nwi_wetlands)

This data set represents the approximate location and type of wetlands and deepwater habitats in New York State.

ouest_mask (haitiegov:ouest-mask)

Peru Migration From Acopalca (care:per-acopalca-flow)

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

Peru Migration From Paccha (care:per-paccha-flow)

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

Peru Source Villages (care:per-villages)

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

Port-a-Piment Watershed

Places of worship (nyserda:places_of_worship)

The Places of Worship data set displays the locations of different places of worship as well as their religions affiliation.

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.

Power transmission lines (nyserda:power_transmission_lines)

The Power Transmission Lines layer was divided into three sub-layers. The Power Transmission Category data set describes the different amounts of electric potential difference (voltage measured in kilovolts) for each power station or line that is within the study area.

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.

Public libraries (nyserda:public_libraries)

The Public libraries data set includes library system and structure information for all public librarires in New York State.

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.

Railroad passenger stations (nyserda:railroad_passenger_stations)

Railroad passenger stations data layer was divided into three sub-layers. The Railroad Passenger Stations Location data set displays the locations of all railroad stations in the study area. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1263 in April 2014. The Railroad Passenger Stations Operator data set displays the different operators of the railroad track lines within New York State. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1263 in April 2014. The Railroad Passenger Stations Use data set displays the different uses for each of the railroad tracks in the counties bordering the Hudson River. The data was obtained from NYS GIS Clearinghouse at https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1263 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.

Roads (nyserda:roads)

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

Schools (nyserda:schools)

The Schools data layer was divided into three sub-layers. The School Grades Housed data set describes the different ages of students in the different schools within the study area. The data was obtained from the New York State Education Department at http://www.nysed.gov/admin/bedsdata.html in January 2014 School Records data set shows the location of the schools within the study area as well as whether the school is a public private or charter school. The data was obtained from the New York State Education Department at http://www.nysed.gov/admin/bedsdata.html in January 2014 The School Location data set displays the locations of all schools within the study area. The data was obtained from the New York State Education Department at http://www.nysed.gov/admin/bedsdata.html in January 2014.

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.

Municipal Level Social Vulnerability Index (nyserda:sovi_municipality)

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_est_mask (haitiegov:sud-est-mask)

sud_mask (haitiegov:sud-mask)

Thailand Migration from Don Moon (care:tha-don-moon-flow)

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

Thailand Unique Points (care:tha-unique-points)

Thailand Study Villages (care:tha-villages)

Tanzania Migration From Bangalala (care:tza-bangalala-flow)

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

Tanzania Unique Points (care:tza-unique-points)

Tanzania Study Villages (care:tza-villages)

Tanzania Migration From Vudee (care:tza-vudee-flow)

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

Vietnam Unique Points (care:vnm-unique-points)

Vietnam Study Villages (care:vnm-villages)

Water Well (nyserda:water_well)

Points and attributes for water wells in New York State. Data regarding water wells has been collected since April 2000 and the dataset does not include information on wells located in Nassau, Suffolk, Kings, and Queens counties.

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.

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