GLWS 2.0: A global product that provides total water storage anomalies, groundwater, soil moisture and surface water with a spatial resolution of 0.5° from 2003 to 2019
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| Format: | Dataset Open Access |
| Language: | en |
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PANGAEA
2023
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| _version_ | 1867169153962475520 |
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| author | Gerdener, Helena Schulze, Kerstin Kusche, Jürgen |
| author_facet | Gerdener, Helena Schulze, Kerstin Kusche, Jürgen |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | The global land water storage (GLWS) data set is produced by assimilating (Eicker at al., 2014) gridded GRACE and GRACE-FO-derived total water storage anomalies (TWSA) into the WaterGAP global hydrological model using the Parallel Data Assimilation Framework (PDAF, Nerger and Hiller, 2013). The resulting data set represents thus an optimal synthesis of GRACE data and all data sets that went into the hydrological model. This synthesis seeks to fit GRACE (-FO) TWSA grids within error bars, and at the same time it solves the horizontal and vertical water balances as represented in the hydrological model, again within error bars. To this end, the uncertainty of the hydrological model simulation is represented via an n-member ensemble, where we take into account the uncertainty of forcing (precipitation and radiation) data as well as the uncertainty of some model calibration parameters. As a result, when no GRACE (-FO) data is available, the GLWS data set represents the mean – or the median additionally provided - of an ensemble where each member is dynamically consistent with the model. It is important to understand that this mean/median depends on the ensemble creation and thus will differ from published WaterGAP standard runs, even if there is no GRACE data within a particular month. It is also important to understand the assimilation-derived GLWS data set does not represent a simple downscaling of the GRACE data, i.e. spatial smoothing of GLWS does not necessarily correspond to GRACE (-FO) TWSA. The monthly level 3 GLWS data represent the total water storage anomaly (TWSA) on 0.5° grids and level 2 GLWS data represent groundwater, soil moisture and surface water. They are provided now for 01/2003 to 12/2019. Additionally, the standard deviation is provided (computed from the ensemble). As default, GLWS is derived from the ensemble mean, here, we additionally provide the ensemble median. The main updates with respect to the release 001 were the use of an updated version of WaterGAP as well as minor bug fixes in the assimilation. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_954742 |
| institution | PANGAEA |
| language | en |
| publishDate | 2023 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | GLWS 2.0: A global product that provides total water storage anomalies, groundwater, soil moisture and surface water with a spatial resolution of 0.5° from 2003 to 2019 Gerdener, Helena Schulze, Kerstin Kusche, Jürgen Data Assimilation; File content; GLWS; GRACE; GRACE-FO; groundwater; netCDF file; netCDF file (File Size); Soil Moisture; surface water; total water storage anomalies; WaterGAP The global land water storage (GLWS) data set is produced by assimilating (Eicker at al., 2014) gridded GRACE and GRACE-FO-derived total water storage anomalies (TWSA) into the WaterGAP global hydrological model using the Parallel Data Assimilation Framework (PDAF, Nerger and Hiller, 2013). The resulting data set represents thus an optimal synthesis of GRACE data and all data sets that went into the hydrological model. This synthesis seeks to fit GRACE (-FO) TWSA grids within error bars, and at the same time it solves the horizontal and vertical water balances as represented in the hydrological model, again within error bars. To this end, the uncertainty of the hydrological model simulation is represented via an n-member ensemble, where we take into account the uncertainty of forcing (precipitation and radiation) data as well as the uncertainty of some model calibration parameters. As a result, when no GRACE (-FO) data is available, the GLWS data set represents the mean – or the median additionally provided - of an ensemble where each member is dynamically consistent with the model. It is important to understand that this mean/median depends on the ensemble creation and thus will differ from published WaterGAP standard runs, even if there is no GRACE data within a particular month. It is also important to understand the assimilation-derived GLWS data set does not represent a simple downscaling of the GRACE data, i.e. spatial smoothing of GLWS does not necessarily correspond to GRACE (-FO) TWSA. The monthly level 3 GLWS data represent the total water storage anomaly (TWSA) on 0.5° grids and level 2 GLWS data represent groundwater, soil moisture and surface water. They are provided now for 01/2003 to 12/2019. Additionally, the standard deviation is provided (computed from the ensemble). As default, GLWS is derived from the ensemble mean, here, we additionally provide the ensemble median. The main updates with respect to the release 001 were the use of an updated version of WaterGAP as well as minor bug fixes in the assimilation. |
| title | GLWS 2.0: A global product that provides total water storage anomalies, groundwater, soil moisture and surface water with a spatial resolution of 0.5° from 2003 to 2019 |
| topic | Data Assimilation; File content; GLWS; GRACE; GRACE-FO; groundwater; netCDF file; netCDF file (File Size); Soil Moisture; surface water; total water storage anomalies; WaterGAP |
| url | https://doi.org/10.1594/PANGAEA.954742 |