Effective Ksat and Storage for CONUS


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Created: Mar 29, 2021 at 1:14 p.m.
Last updated: Jul 29, 2021 at 3:27 p.m.
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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.

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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

Tashie, A., T. Pavelsky, L. Band, S. Topp (2021). Effective Ksat and Storage for CONUS, HydroShare, http://www.hydroshare.org/resource/115409dbe8354e78a2c2219d32e2b9de

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
CC-BY

Comments

Arik Tashie 1 year, 3 months ago

To all:

If 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

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