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Hydrologic modeling and field data for studying the role of subsurface critical zone structure on hydrological partitioning
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| Type: | Resource | |
| Storage: | The size of this resource is 726.5 KB | |
| Created: | Oct 17, 2023 at 9:03 p.m. | |
| Last updated: | Oct 23, 2023 at 1:16 p.m. | |
| DOI: | 10.4211/hs.d1537789fea8421aa3fe1cd2ec155b57 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 195 |
| Downloads: | 11 |
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Abstract
This dataset contains the codes and data used in the manuscript “Influence of Subsurface Critical Zone Structure on Hydrological Partitioning in Mountainous Headwater Catchments” submitted to Geophysical Research Letters. The software requirement are summarized in requirement.txt; hydrologic modeling input data are in the folder TLnewtest2sfb2; the observation data used in the simulation are indicated as comments in the python scripts. Note that the hydrologic modeling was run in HPC (Linux system) with parallel computing.
Below are the abstract of the manuscript:
“Headwater catchments play a vital role in regional water supply and ecohydrology, and a quantitative understanding of the hydrological partitioning in these catchments is critically needed, particularly under a changing climate. Recent studies have highlighted the importance of subsurface critical zone (CZ) structure in modulating the partitioning of precipitation in mountainous catchments; however, few existing studies have explicitly taken into account the 3D subsurface CZ structure. In this study, we designed realistic synthetic catchment models based on seismic velocity-estimated 3D subsurface CZ structures. Integrated hydrologic modeling is then used to study the effect of the shape of the weathered bedrock bottom on various hydrologic fluxes and storages in mountainous headwater catchments. Numerical results show that the shape of the weathered bedrock bottom not only affects the magnitude but also the peak time of both streamflow and subsurface dynamic storage.”
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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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