Nazari, Sara; Kruse, Irene Livia; Moosdorf, Nils: Grid-based rain-fed annual global groundwater recharge [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.957447 (dataset in review)
Abstract:
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).
Keyword(s):
Supplement to:
Nazari, Sara; Kruse, Irene Livia; Moosdorf, Nils (submitted): Spatiotemporal dynamics of global groundwater recharge from 2001 to 2020. Journal of Hydrology
Source:
Amante, Christopher; Eakins, Barry W (2009): ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA, https://doi.org/10.7289/V5C8276M
Hengl, Tomislav; Mendes de Jesus, Jorge; Heuvelink, Gerard B M; Gonzalez, Maria Ruiperez; Kilibarda, Milan; Blagotić, Aleksandar; Shangguan, Wei; Wright, Marvin N; Geng, Xiaoyuan; Bauer-Marschallinger, Bernhard; Guevara, Mario Antonio; Vargas, Rodrigo; MacMillan, Robert A; Batjes, Niels H; Leenaars, Johan G B; Ribeiro, Eloi; Wheeler, Ichsani; Mantel, Stephan; Kempen, Bas; Bond-Lamberty, Ben (2017): SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12(2), e0169748, https://doi.org/10.1371/journal.pone.0169748
Huffman, G J; Stocker, E F; Bolvin, D T; Nelkin, E J; Tan, Jackson (2019): GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06, edited by Andrey Savtchenko, Greenbelt, MD, accessed: 2022-04-01 [dataset]. NASA Goddard Earth Sciences Data and Information Services Center, https://doi.org/10.5067/GPM/IMERGDF/DAY/06
Muñoz Sabater, J (2019): ERA5-Land hourly data from 2001 to present, accessed on 2022-04-02 [dataset]. ECMWF, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/CDS.E2161BAC
Simons, Gijs; Koster, R D; Droogers, P (2020): Hihydrosoil v2. 0-high resolution soil maps of global hydraulic properties. FutureWater, Technical Report 213
Stacke, Tobias; Hagemann, Stefan (2021): HydroPy (v1.0): a new global hydrology model written in Python. Geoscientific Model Development, 14(12), 7795-7816, https://doi.org/10.5194/gmd-14-7795-2021
References:
Lehner, Bernhard; Grill, E V (2013): Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15), 2171-2186, https://doi.org/10.1002/hyp.9740
Related code / software:
Nazari, Sara (2024): Source code for the Global Groundwater rain-fed Recharge (GGR) model [software]. Zenodo, https://doi.org/10.5281/ZENODO.13225038
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | Variable | Variable | Nazari, Sara | Python | ||
| 2 | File content | Content | Nazari, Sara | Python | ||
| 3 | Resolution | Resolution | Nazari, Sara | Python | temporal | |
| 4 | Resolution | Resolution | Nazari, Sara | Python | spatial | |
| 5 | Horizontal datum, projection stored in file | Horizontal datum in file | Nazari, Sara | Python | ||
| 6 | Longitude, westbound | Longitude west | Nazari, Sara | Python | ||
| 7 | Longitude, eastbound | Longitude east | Nazari, Sara | Python | ||
| 8 | Latitude, southbound | Latitude south | Nazari, Sara | Python | ||
| 9 | Latitude, northbound | Latitude north | Nazari, Sara | Python | ||
| 10 | Year of analysis | Year analysis | a AD | Nazari, Sara | Python | start |
| 11 | Year of analysis | Year analysis | a AD | Nazari, Sara | Python | end |
| 12 | Data type | Data type | Nazari, Sara | Python | ||
| 13 | Geospatial vector, shapefiles | Shapefile | Nazari, Sara | Python | ||
| 14 | Geospatial vector, shapefiles (File Size) | Shapefile (Size) | Bytes | Nazari, Sara | Python | |
| 15 | Geospatial vector, shapefiles (MD5 Hash) | Shapefile (Hash) | Nazari, Sara | Python |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0) (License comes into effect after moratorium ends)
Status:
Curation Level: Enhanced curation (CurationLevelC) * Processing Level: PANGAEA data processing level 4 (ProcLevel4)
Size:
38 data points