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| Created: | Mar 29, 2021 at 1:14 p.m. | |
| Last updated: | Mar 31, 2023 at 9:29 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 1808 |
| Downloads: | 133 |
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| Comments: | 4 comments |
Abstract
In land surface models, the hydraulic properties of the subsurface are commonly estimated according to the texture of soils at the earth’s surface. This approach ignores macropores, fracture flow, heterogeneity, and the effects of variable distribution of water in the subsurface on effective watershed-scale hydraulic variables. Using hydrograph recession analysis, we empirically constrain estimates of watershed-scale effective hydraulic conductivities (K) and effective drainable aquifer storages (S) of all reference watersheds in the continental US for which sufficient streamflow data are available (n=1561). Then, we use machine learning methods to model these properties across the continental. Model validation results in high confidence for estimates of log(K) (r2 > 0.89; 1% < bias < 9%) and reasonable confidence for S (r2 > 0.83; -70% < bias < -18%). Our estimates of effective K are, on average, two orders of magnitude higher than comparable soils-texture based estimates of average K, confirming the importance of soil structure and preferential flow pathways at the watershed scale. Our estimates of effective S compare favorably with recent global estimates of mobile groundwater and are spatially heterogeneous (5-3355mm). Because estimates of S are much lower than the global maximums generally used in land surface models (e.g., 5000mm in Noah-MP), they may serve both to limit model spin-up time and to constrain model parameters to more realistic values. These results represent the first attempt to constrain estimates of watershed-scale effective hydraulic variables that are necessary for the implementation of land surface models for the entire continental US.
Subject Keywords
Content
readme.txt
# Effective Ksat and Storage for CONUS
## Ksat_Storage_for_CONUS_inc_infils.feather
Column 1: the 12-digit hydrologic unit code of each watersheds
Column 2: COMID of each watershed
Columns 3-123: catchment level summary data for each watershed, where available. These data are from USGS NHD Plus Version 2.1 database [Wieczorek et al. 2018].
The metadata for these data are available at https://www.sciencebase.gov/catalog/item/5669a79ee4b08895842a1d47
Columns 124 to 242: upslope watershed summary data for each watershed, where available. These data are from USGS NHD Plus Version 2.1 database [Wieczorek et al. 2018].
The metadata for these data are available at https://www.sciencebase.gov/catalog/item/5669a79ee4b08895842a1d47
Columns 243-245: ratios derived from the above data. These derivations are detailed in the accompanying research article [Tashie et al. 2021]
Columns 246-249: ("Storage_Dry", "Storage_Wet", "Ksat_Dry", and "Ksat_Wet") give estimates of watershed scale effective drainable storage and saturated hydraulic conductivity during dry and wet periods.
Units are in [mm] for Storage, and in [cm/s] for Ksat
Column 269: "FLAG" designates flagged data:
"est_from_HLRs" indicates catchments where insufficient data were available to directly estimate Ksat of Storage and therefore these values were estimated according to average values as aggregated by Hydrologic Landscape Region
"CONUS_avg" indicates catchments where data were insufficient and there was no designating Hydrologic Landscape Region, so values were estimated according to the average values of the continental US (CONUS)
Wieczorek, M.E., Jackson, S.E., and Schwarz, G.E., 2018, Select Attributes for NHDPlus Version 2.1 Reach Catchments and Modified Network Routed Upstream Watersheds for the Conterminous United States (ver. 3.0, January 2021): U.S. Geological Survey data release, https://doi.org/10.5066/F7765D7V.
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
Comments
Arik Tashie 3 years, 5 months ago
To all:
ReplyIf you have any issues downloading or interpreting this data, please feel free to reach out:
tashi002@ua.edu
This is the first large data set I've published, so I'd love to hear feedback about how to make the user experience easier / less painful.
Best,
Arik
Ward Sanford 1 year, 7 months ago
Hey Arik:
ReplyDo you have a .csv version of your files?
I cannot open a .feather file.
Ward Sanford -- USGS
Arik Tashie 1 year, 7 months ago
Hi Ward,
ReplySorry about that! I have no idea why I decided to share the data as a feather file instead of a csv. I've added a csv version, and if you have any questions please don't hesitate to reach out.
All the best,
Arik (arik@climate.ai)
Arik Tashie 1 year, 7 months ago
To any future users: I am no longer at tashi002@ua.edu. Please email at arik@climate.ai.
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