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| Type: | Resource | |
| Storage: | The size of this resource is 1016.1 MB | |
| Created: | Apr 10, 2023 at 7:48 a.m. | |
| Last updated: | Aug 25, 2023 at 2:58 p.m. | |
| DOI: | 10.4211/hs.db187b7e328c4158879926d8f9a6dccd | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 688 |
| Downloads: | 37 |
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Abstract
Groundwater overdraft gives rise to multiple adverse impacts including land subsidence and permanent groundwater storage loss. Existing methods have been unable to characterize groundwater storage loss at the global scale with sufficient resolution to be relevant for local studies. Here we explore the interrelation between groundwater stress, aquifer depletion, and land subsidence using remote sensing and model-based datasets with a machine learning approach. The developed model predicts global land subsidence magnitude at high spatial resolution (~2 km) and provides a first-order estimate of aquifer storage loss due to consolidation of ~17 km3/year globally. China, the United States, and Iran account for the majority of groundwater storage loss due to consolidation. The model quantifies key drivers of subsidence and has high predictive accuracy, with an F1-score of 0.83 on the validation set. Roughly 73% of the mapped subsidence occurs over cropland and urban areas, highlighting the need for sustainable groundwater management practices over these areas. The results of this study aid in assessing the spatial extents of subsidence in known subsiding areas, and in locating unknown groundwater stressed regions.
Subject Keywords
Coverage
Spatial
Content
README.txt
The reposatory includes datasets and results for a machine learning model to generate global map of subsidence (~2 km resoultion) induced by groundwater pumping. ############################### This research is under review in a peer-reviewed journal. We request users' discretion in using any data available in this repository. ############################### Data description: - csv_training.zip contains the training dataset used in the model in csv format. - Predictors.zip contains the input variables used in the model in GeoTIFF format. - Reference_files.zip consists of reference shapefile (GIS format), csv, and raster (GeoTIFF) needed for running the model. - Results.zip the produced land subsidence and subsidence probability data (in GeoTIFF format) generated by the model. ############################## The model's code is available in the following GitHUB repository https://github.com/mdfahimhasan/Global-Subsidence-Groundwate ############################## For more information contact: Fahim.Hasan@colostate.edu / fahimhasan107@gmail.com
Related Resources
| The content of this resource can be executed by | Hasan, M. F., Smith, R., Vajedian, S., Pommerenke, R., Majumdar, S., Global Land Subsidence Mapping Reveals Widespread Loss of Aquifer Storage Capacity, GitHub (2023) DOI: 10.5281/zenodo.8280482 |
| The content of this resource can be executed by | https://github.com/mdfahimhasan/Global-Subsidence-Groundwater |
| This resource has been replaced by a newer version | Hasan, M. F., R. Smith, S. Vajedian, R. Pommerenke, S. Majumdar (2023). Global Land Subsidence Mapping Reveals Widespread Loss of Aquifer Storage Capacity Datasets, HydroShare, http://www.hydroshare.org/resource/dc7c5bfb3a86479b889d3b30ab0e4ef7 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Geospatial-Intelligence Agency | Global Land Subsidence Mapping Reveals Widespread Groundwater Storage Loss and Supplemental | HM0476-21-1-0001 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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