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| Format: | Dataset Open Access |
| Language: | en |
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PANGAEA
2024
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| Online Access: | https://doi.org/10.1594/PANGAEA.957447 |
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| _version_ | 1867171884478496768 |
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| author | Nazari, Sara Kruse, Irene Livia Moosdorf, Nils |
| author_facet | Nazari, Sara Kruse, Irene Livia Moosdorf, Nils |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | The data is the output of the Global Groundwater Rain-fed Recharge (GGR) model. The GGR model is a grid-based model and is developed and implemented in Python to simulate the daily rain-fed groundwater recharge. The GGR model calculates the exchange of water between topsoil and atmosphere, as well as surface runoff, topsoil recharge, water volume in soil layers, subsoil infiltration, capillary rise from the subsoil to the topsoil, and groundwater recharge, all on a daily time step and grid-based values. The model covers the spatial extent from 180.0°W to 180.0°E longitudes and 60.0°N to 60.0°S latitudes and a temporal range from January 2001 to December 2020 with a spatial resolution of 0.1°×0.1° and daily temporal resolution. The output provided here is the main result of the GGR model and is the annual per river basins (HydroBASINS level 4, Lehner, 2013) rain-fed groundwater recharge (R_gw) from 2001 to 2020, the temporal trend of groundwater rechage (S_R_gw), using linear regression analysis, and the p-value (P_R_gw). |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_957447 |
| institution | PANGAEA |
| language | en |
| publishDate | 2024 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Grid-based rain-fed annual global groundwater recharge Nazari, Sara Kruse, Irene Livia Moosdorf, Nils Data type; File content; Geospatial vector, shapefiles; Geospatial vector, shapefiles (File Size); Geospatial vector, shapefiles (MD5 Hash); global; global groundwater recharge; global hydrological cycle model; groundwater to atmosphere; Horizontal datum, projection stored in file; hydrological modelling; Latitude, northbound; Latitude, southbound; Longitude, eastbound; Longitude, westbound; Python; Resolution; Variable; Year of analysis The data is the output of the Global Groundwater Rain-fed Recharge (GGR) model. The GGR model is a grid-based model and is developed and implemented in Python to simulate the daily rain-fed groundwater recharge. The GGR model calculates the exchange of water between topsoil and atmosphere, as well as surface runoff, topsoil recharge, water volume in soil layers, subsoil infiltration, capillary rise from the subsoil to the topsoil, and groundwater recharge, all on a daily time step and grid-based values. The model covers the spatial extent from 180.0°W to 180.0°E longitudes and 60.0°N to 60.0°S latitudes and a temporal range from January 2001 to December 2020 with a spatial resolution of 0.1°×0.1° and daily temporal resolution. The output provided here is the main result of the GGR model and is the annual per river basins (HydroBASINS level 4, Lehner, 2013) rain-fed groundwater recharge (R_gw) from 2001 to 2020, the temporal trend of groundwater rechage (S_R_gw), using linear regression analysis, and the p-value (P_R_gw). |
| title | Grid-based rain-fed annual global groundwater recharge |
| topic | Data type; File content; Geospatial vector, shapefiles; Geospatial vector, shapefiles (File Size); Geospatial vector, shapefiles (MD5 Hash); global; global groundwater recharge; global hydrological cycle model; groundwater to atmosphere; Horizontal datum, projection stored in file; hydrological modelling; Latitude, northbound; Latitude, southbound; Longitude, eastbound; Longitude, westbound; Python; Resolution; Variable; Year of analysis |
| url | https://doi.org/10.1594/PANGAEA.957447 |