Өөрчлөлтүүд

              
    

          
          
        
        

            
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  "agreement": "true",
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  "agreement": "true",
            
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  "alternative_identifier": "DOI: 10.1016/j.ejrh.2024.101879",
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  "alternative_identifier": "DOI: 10.1016/j.ejrh.2024.101879",
            
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  "author": null,
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  "author": null,
            
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  "author_email": null,
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  "author_email": null,
            
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  "authors": [
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  "authors": [
            
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    {
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    {
            
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      "author_department": "Department of Plant and Soil Sciences",
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      "author_department": "Department of Plant and Soil Sciences",
            
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      "author_email": "cindy.viviers@tuks.co.za",
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      "author_email": "cindy.viviers@tuks.co.za",
            
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      "author_name": "Cindy",
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      "author_name": "Cindy",
            
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      "author_organization": "University of Pretoria",
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      "author_organization": "University of Pretoria",
            
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      "author_surname": "Viviers",
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      "author_surname": "Viviers",
            
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      "contact_same_as_author": "true"
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      "contact_same_as_author": "true"
            
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    },
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    },
            
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    {
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    {
            
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      "author_department": "Water Science",
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      "author_department": "Water Science",
            
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      "author_email": "vanderLaanM@arc.agric.za",
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      "author_email": "vanderLaanM@arc.agric.za",
            
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      "author_name": "Michael",
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      "author_name": "Michael",
            
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      "author_organization": "Agricultural Research Council",
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      "author_organization": "Agricultural Research Council",
            
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      "author_surname": "van der Laan"
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      "author_surname": "van der Laan"
            
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    },
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    },
            
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    {
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    {
            
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      "author_department": "",
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      "author_department": "",
            
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      "author_email": "michael.vanderlaan@up.ac.za",
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      "author_email": "michael.vanderlaan@up.ac.za",
            
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      "author_name": "Zaheed",
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      "author_name": "Zaheed",
            
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      "author_organization": "IBM Research Africa",
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      "author_organization": "IBM Research Africa",
            
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      "author_surname": "Gaffoor"
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      "author_surname": "Gaffoor"
            
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    },
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    },
            
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    {
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    {
            
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      "author_department": "Department of Geology",
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      "author_department": "Department of Geology",
            
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      "author_email": "",
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      "author_email": "",
            
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      "author_name": "Matthys",
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      "author_name": "Matthys",
            
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      "author_organization": "University of Pretoria",
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      "author_organization": "University of Pretoria",
            
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      "author_surname": "Dippenaar"
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      "author_surname": "Dippenaar"
            
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    }
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    }
            
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  ],
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  ],
            
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  "citation_title": "VIVIERS C, VAN DER LAAN M, GAFFOOR Z and 
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  "citation_title": "VIVIERS C, VAN DER LAAN M, GAFFOOR Z and 
            
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DIPPENAAR M (2024) Downscaling and validating GLDAS groundwater 
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DIPPENAAR M (2024) Downscaling and validating GLDAS groundwater 
            
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storage anomalies by integrating precipitation for recharge and actual 
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storage anomalies by integrating precipitation for recharge and actual 
            
40
evapotranspiration for discharge. Journal of Hydrology: Regional 
40
evapotranspiration for discharge. Journal of Hydrology: Regional 
            
41
Studies 54 101879. https://doi.org/10.1016/j.ejrh.2024.101879.",
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Studies 54 101879. https://doi.org/10.1016/j.ejrh.2024.101879.",
            
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  "contact_person": [
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  "contact_person": [
            
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    {
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    {
            
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      "contact_department": "Department of Plant and Soil Sciences",
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      "contact_department": "Department of Plant and Soil Sciences",
            
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      "contact_email": "cindy.viviers@tuks.co.za",
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      "contact_email": "cindy.viviers@tuks.co.za",
            
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      "contact_name": "Cindy",
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      "contact_name": "Cindy",
            
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      "contact_orgnization": "University of Pretoria"
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      "contact_orgnization": "University of Pretoria"
            
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    }
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    }
            
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  ],
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  ],
            
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  "creator_user_id": "bc28a33f-dc21-4795-a57f-da7cd1aa23ea",
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  "creator_user_id": "bc28a33f-dc21-4795-a57f-da7cd1aa23ea",
            
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  "data_classification": "static",
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  "data_classification": "static",
            
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  "data_collection_organization": "Agricultural Research Council",
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  "data_collection_organization": "Agricultural Research Council",
            
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  "data_reference_date": [
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      "data_reference_date_to": ""
            
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    }
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    }
            
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  ],
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  ],
            
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  "data_structure_category": "structured",
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  "data_structure_category": "structured",
            
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  "dataset_language": "English",
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  "dataset_language": "English",
            
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  "dataset_license_url": 
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  "dataset_license_url": 
            
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"https://creativecommons.org/licenses/by-sa/4.0/",
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"https://creativecommons.org/licenses/by-sa/4.0/",
            
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  "did_author_or_contact_organization_collect_the_data": "false",
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  "email": "vanderLaanM@arc.agric.za",
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  "email": "vanderLaanM@arc.agric.za",
            
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  "groups": [],
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  "groups": [],
            
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  "id": "d9a0306f-a0d5-4635-b424-cc7ef1f0830e",
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  "id": "d9a0306f-a0d5-4635-b424-cc7ef1f0830e",
            
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  "isopen": false,
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  "isopen": false,
            
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  "keywords": "groundwater storage anomaly, GLDAS\u20112.2, GRACE, 
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  "keywords": "groundwater storage anomaly, GLDAS\u20112.2, GRACE, 
            
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CHIRPS precipitation, MODIS evapotranspiration, machine learning, 
69
CHIRPS precipitation, MODIS evapotranspiration, machine learning, 
            
70
random forest, downscaling, Google Earth Engine, South Africa, 
70
random forest, downscaling, Google Earth Engine, South Africa, 
            
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Steenkoppies Catchment",
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Steenkoppies Catchment",
            
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  "license": "Open (Creative Commons)",
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  "license": "Open (Creative Commons)",
            
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  "license_id": null,
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  "license_id": null,
            
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  "license_title": null,
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  "license_title": null,
            
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  "maintainer": null,
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  "maintainer": null,
            
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  "maintainer_email": null,
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  "metadata_created": "2026-02-19T14:07:44.857891",
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  "metadata_created": "2026-02-19T14:07:44.857891",
            
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  "metadata_modified": "2026-06-18T19:41:31.030323",
            
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  "minimum_maximum_extent": [
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  "minimum_maximum_extent": [
            
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    {
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    {
            
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      "maximum_vertical_extent": "-80",
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      "maximum_vertical_extent": "-80",
            
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      "minimum_vertical_extent": "0"
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      "minimum_vertical_extent": "0"
            
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    }
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    }
            
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  ],
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  ],
            
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  "name": 
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  "name": 
            
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l-for-downscaling-and-validating-gldas-groundwater-storage-anomalies",
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l-for-downscaling-and-validating-gldas-groundwater-storage-anomalies",
            
87
  "notes": "This dataset underpins a framework for statistically 
87
  "notes": "This dataset underpins a framework for statistically 
            
88
downscaling GLDAS\u20112.2 groundwater storage anomalies (GWSA) from a 
88
downscaling GLDAS\u20112.2 groundwater storage anomalies (GWSA) from a 
            
89
coarse 0.25\u00b0 spatial resolution to a finer 0.05\u00b0 resolution 
89
coarse 0.25\u00b0 spatial resolution to a finer 0.05\u00b0 resolution 
            
90
using a Random Forest machine\u2011learning algorithm. The approach 
90
using a Random Forest machine\u2011learning algorithm. The approach 
            
91
integrates only two freely available remotely sensed inputs: CHIRPS 
91
integrates only two freely available remotely sensed inputs: CHIRPS 
            
92
precipitation to represent groundwater recharge processes and MODIS 
92
precipitation to represent groundwater recharge processes and MODIS 
            
93
actual evapotranspiration (ET\u2090) to represent discharge 
93
actual evapotranspiration (ET\u2090) to represent discharge 
            
94
processes.\r\n\r\nThe study area is the Steenkoppies Catchment 
94
processes.\r\n\r\nThe study area is the Steenkoppies Catchment 
            
95
(quaternary catchment A21F), located approximately 75 km 
95
(quaternary catchment A21F), located approximately 75 km 
            
96
south\u2011west of Pretoria, South Africa, and includes both karst 
96
south\u2011west of Pretoria, South Africa, and includes both karst 
            
97
(dolomitic) and intergranular/fractured aquifers. Monthly 
97
(dolomitic) and intergranular/fractured aquifers. Monthly 
            
98
GLDAS\u20112.2 GWSA data (2003\u20132021) were processed in Google 
98
GLDAS\u20112.2 GWSA data (2003\u20132021) were processed in Google 
            
99
Earth Engine, with precipitation and ET\u2090 standardised to 
99
Earth Engine, with precipitation and ET\u2090 standardised to 
            
100
anomalies relative to a 2004\u20132009 baseline. Temporal lag analysis 
100
anomalies relative to a 2004\u20132009 baseline. Temporal lag analysis 
            
101
identified optimal lags of three months for precipitation and two 
101
identified optimal lags of three months for precipitation and two 
            
102
months for ET\u2090, which significantly improved model performance 
102
months for ET\u2090, which significantly improved model performance 
            
103
and reduced residuals.\r\n\r\nDownscaled GWSA estimates were validated 
103
and reduced residuals.\r\n\r\nDownscaled GWSA estimates were validated 
            
104
against in situ groundwater level measurements from the Department of 
104
against in situ groundwater level measurements from the Department of 
            
105
Water and Sanitation monitoring network, converted to storage 
105
Water and Sanitation monitoring network, converted to storage 
            
106
anomalies using aquifer\u2011specific specific yield values. The 
106
anomalies using aquifer\u2011specific specific yield values. The 
            
107
downscaled product preserved mass conservation, captured enhanced 
107
downscaled product preserved mass conservation, captured enhanced 
            
108
seasonal amplitudes, and achieved improved agreement with observations 
108
seasonal amplitudes, and achieved improved agreement with observations 
            
109
(up to r = 0.6, RMSE \u2248 40 mm). The dataset demonstrates strong 
109
(up to r = 0.6, RMSE \u2248 40 mm). The dataset demonstrates strong 
            
110
potential for monitoring groundwater storage dynamics in 
110
potential for monitoring groundwater storage dynamics in 
            
111
data\u2011scarce regions, complementing rather than replacing in situ 
111
data\u2011scarce regions, complementing rather than replacing in situ 
            
112
observations. The adaptable Google Earth Engine code is openly 
112
observations. The adaptable Google Earth Engine code is openly 
            
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available for application in other regions.",
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available for application in other regions.",
            
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    "description": "The University of Pretoria (UP) is one of 
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    "description": "The University of Pretoria (UP) is one of 
            
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Africa\u2019s top universities and the largest contact university in 
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Africa\u2019s top universities and the largest contact university in 
            
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South Africa. We produce socially impactful research to find solutions 
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South Africa. We produce socially impactful research to find solutions 
            
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for the world\u2019s most pressing issues. We have a high quality of 
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for the world\u2019s most pressing issues. We have a high quality of 
            
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teaching and learning in the classroom, online, or in communities. We 
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teaching and learning in the classroom, online, or in communities. We 
            
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have support in place for our students to graduate on time as 
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have support in place for our students to graduate on time as 
            
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well-rounded, responsible citizens fully prepared for the world beyond 
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well-rounded, responsible citizens fully prepared for the world beyond 
            
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university.",
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university.",
            
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  "publication_date": "2024-06-21",
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  "publication_date": "2024-06-21",
            
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  "publisher": "Water Research Commission",
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  "publisher": "Water Research Commission",
            
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      "dataset_description": "Google Earth Engine code for downscaling 
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      "dataset_description": "Google Earth Engine code for downscaling 
            
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and validating GLDAS groundwater storage anomalies.",
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and validating GLDAS groundwater storage anomalies.",
            
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      "is_data_supplementary": "False",
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      "resource_name": "Model for downscaling and validating GLDAS 
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      "resource_name": "Model for downscaling and validating GLDAS 
            
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groundwater storage anomalies",
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groundwater storage anomalies",
            
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      "size": 67378,
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      "size": 67378,
            
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      "state": "active",
            
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      "url": 
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      "url": 
            
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-b728-4ac086767e1b/download/downscaling-gldas-2.2-groundwater-storag",
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-b728-4ac086767e1b/download/downscaling-gldas-2.2-groundwater-storag",
            
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      "url_type": "upload",
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-34.8212], [16.4699, -22.1265]]]}",

            
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  "state": "active",
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  "state": "active",
            
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  "tags": [],
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  "tags": [],
            
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  "title": "Model for downscaling and validating GLDAS groundwater 
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  "title": "Model for downscaling and validating GLDAS groundwater 
            
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storage anomalies",
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storage anomalies",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "uploader_estimation_of_extent_of_processing": "refined",
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  "uploader_estimation_of_extent_of_processing": "refined",
            
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  "version": null,
            
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  "vertical_extent_datum": "mbgl",
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  "vertical_extent_datum": "mbgl",
            
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  "wrc_project_number": "WRC Project Number C2020/2021\u201100440",
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  "wrc_project_number": "WRC Project Number C2020/2021\u201100440",
            
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  "wro_theme": "groundwater"
182
  "wro_theme": "groundwater"
            
186
}
183
}