Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure The resulting relative humidity has been aggregated to decadal averages. Each month is divided into three decades: the first decade of a month covers days 1-10, the second decade covers days 11-20, and the third decade covers days 21-last day of the month. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. The data have been reprojected to EU LAEA. File naming scheme (YYYY = year; MM = month; dD = number of decade): ERA5_land_rh2m_avg_decadal_YYYY_MM_dD.tif Projection + EPSG code: EU LAEA (EPSG: 3035) Spatial extent: north: 6874000 south: -485000 west: 869000 east: 8712000 Spatial resolution: 1000 m Temporal resolution: Decadal Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: https://apps.ecmwf.int/datasets/licences/copernicus/ CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122 Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023) Source data: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Total precipitation: Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Processing steps: The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate proportion of ERA5-Land / aggregated CHELSA 3. interpolate proportion with a Gaussian filter to 30 arc seconds 4. multiply the interpolated proportions with CHELSA Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA. The spatially enhanced daily ERA5-Land data has been aggregated to monthly resolution, by calculating the sum of the precipitation per pixel over each month. File naming: ERA5_land_monthly_prectot_sum_30sec_YYYY_MM_01T00_00_00_int.tif e.g.:ERA5_land_monthly_prectot_sum_30sec_2023_12_01T00_00_00_int.tif The date within the filename is year and month of aggregated timestamp. Pixel values: mm * 10 Scaled to Integer, example: value 218 = 21.8 mm Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 28:18N south: 14:42N west: 17:05W east: 4:49W Temporal extent: January 2019 - December 2023 Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: monthly Lineage: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used. Software used: GRASS GIS 8.3.2 Format: GeoTIFF Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122 Representation type: Grid Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Contact: mundialis GmbH & Co. KG, info@mundialis.de Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure The resulting relative humidity has been aggregated to decadal averages. Each month is divided into three decades: the first decade of a month covers days 1-10, the second decade covers days 11-20, and the third decade covers days 21-last day of the month. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. File naming scheme (YYYY = year; MM = month; dD = number of decade): ERA5_land_rh2m_avg_decadal_YYYY_MM_dD.tif Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Decadal Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: https://apps.ecmwf.int/datasets/licences/copernicus/ CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122 Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023) Source data: - CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m - CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management. The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices. Processing steps: To cover the complete time period from 2019 to 2023 two data products of the Water Bodies product group are processed. Up to December of 2020 the Water Bodies at 10-daily resolution have been used, from January 2021 the Water Bodies at monthly resolution have been used. Both original datasets have been downloaded for the area of Mauritania (NUTS MR) within Latitude-Longitude/WGS84 spatial reference system. Then both datasets have been downsampled to 30 arc seconds (ca. 1000 meter) using the most frequent occuring value. The 10-daily data have been aggregated to monthly resolution using the most frequent occurring value. File naming: Until December 2020: c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif e.g.: c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_2020_12_01T00_00_00.tif From January 2021 on: c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif e.g.: c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_2023_12_01T00_00_00.tif The date within the filename is year and month of aggregated timestamp. NOTE: data for 2023-04 are missing, since they are not available from CLMS Pixel values: 0: Sea 70: Water 255: No water Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 27:17:30N south: 14:43:30N west: 17:04:30W east: 04:48:00W Temporal extent: January 2019 - December 2023 (except: April 2023) Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: monthly Software used: GRASS GIS 8.3.2 Format: GeoTIFF Original dataset license: Generated using European Union's Copernicus Land Monitoring Service information Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Contact: mundialis GmbH & Co. KG, info@mundialis.de Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023). Source data: - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): https://lpdaac.usgs.gov/products/mod13a2v061/ The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. For the time period January 2019 - December 2023, the NDVI layer of the original data has been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". These 16-Day data are then aggregated to monthly temporal resolution using the maximum and reprojected to Latitude-Longitude/WGS84. File naming: ndvi_filt_YYYY_MM_01T00_00_00.tif e.g.: ndvi_filt_2023_12_01T00_00_00.tif The date within the filename is year and month of aggregated timestamp. Pixel values: NDVI * 10000 Scaled to Integer, example: value 6473 = 0.6473 Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 28N south: 14N west: 18W east: 4W Temporal extent: January 2019 - December 2023 Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: monthly Software used: GRASS GIS 8.3.2 Format: GeoTIFF Original dataset license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/mod13a2v061/ Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Contact: mundialis GmbH & Co. KG, info@mundialis.de Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure The resulting relative humidity has been aggregated to monthly averages. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. File naming scheme (YYYY = year; MM = month): ERA5_land_rh2m_avg_monthly_YYYY_MM.tif Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Monthly Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2 Original ERA5-Land dataset license: https://apps.ecmwf.int/datasets/licences/copernicus/ CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122 Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Data is also available in EU LAEA (EPSG: 3035) projection: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/ab06ed25-84af-43c9-b1c3-57e3b6ad8d29 Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure Data provided is the daily averages of relative humidity. This set provides data for the years 2000 - 2004. For other time periods, please see further linked data sets. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. File naming scheme (YYYY = year; MM = month; DD = day): ERA5_land_rh2m_avg_daily_YYYYMMDD.tif Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Daily Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: https://apps.ecmwf.int/datasets/licences/copernicus/ CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122 Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

Overview: Daily maps for global daylight length, calculated for the year 2022. Processing steps: For each day within the year 2022, the photoperiod (sunshine hours on flat terrain) are calculated using the SOLPOS algorithm developed by the National Renewable Energy Laboratory (NREL), USA. Resultant values have been converted from hours to minutes. File naming scheme (DDD = day within year) (min is abbreviation for minute): daylight_min_2022_DDD.tif Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 90 south: -90 west: -180 east: 180 Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Daily Pixel values: unit: minutes Software used: GDAL 3.2.2 and GRASS GIS 8.2.0 Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Reference: National Renewable Energy Laboratory (NREL): SOLPOS 2.0 sun position algorithm (https://www.nrel.gov/grid/solar-resource/solpos.html)