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[Data] The Role of Subsurface Critical Zone Structure on Hydrological Partitioning in a Headwater Mountainous Catchment
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| Type: | Resource | |
| Storage: | The size of this resource is 210.1 KB | |
| Created: | Oct 16, 2023 at 11:17 p.m. | |
| Last updated: | Oct 17, 2023 at 9:05 p.m. | |
| DOI: | 10.4211/hs.711d68471bfb44f78512bc2a8594e002 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 361 |
| Downloads: | 3 |
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Abstract
Codes and data for paper: The Role of Subsurface Critical Zone Structure on Hydrological Partitioning in a Headwater Mountainous Catchment
The paper abstract
Headwater catchments play a vital role in regional water supply, necessitating a comprehensive understanding of hydrological partitioning for sustainable management. Recent studies have highlighted the significance of fractured rock layers in hydrological partitioning. However, the influence of these fractures on hydrological partitioning, particularly in relation to regional stress fields and the distribution of fractures parallel or mirroring the surface, remains largely unknown. This study utilizes geophysics-informed hydrologic modeling to examine the impacts of subsurface structure on hydrological partitioning in a mountainous headwater catchment. Our findings underscore the substantial influence of fractured bedrock on streamflow discharge, rock storage, and deep infiltration. Notably, climate change simulations reveal that catchments with paralleling surface structures may exhibit lower resilience. These findings provide valuable insights for water resource management, emphasizing the need to consider subsurface characteristics, including bedrock fracture distributions, to effectively anticipate and adapt to changing hydrological patterns.
The code and data description:
The code-dependent package see requirement.txt
Note that the code was running in HPC (Linux system) for parallel computing purposes. We cannot promise it can work in the Windows system.
All of model input data are in folder TLnewtest2sfb2
The use of data can be seen in the comment in the python code
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Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| National Science Foundation | RAPID: Monitoring subsurface water storage dynamics associated with the 2023 extreme snowfall events in precipitation-limited systems | EAR#2330004 |
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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