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Carbon fluxes, meteorological, soil variables from GC Flux Tower, IMLCZO/CINet

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Owners:		This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource.
Type:	Resource
Storage:	The size of this resource is 11.4 MB
Created:	May 22, 2024 at 3:50 p.m.
Last updated:	Jun 05, 2024 at 12:36 p.m. (Metadata update)
Published date:	Jun 05, 2024 at 12:36 p.m.
DOI:	10.4211/hs.1752781278c74d4ba52d986a73070c04
Citation:	See how to cite this resource

Sharing Status:	Published
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Abstract

15-minute time-series data from flux tower at Goose Creek site, originally part of the NSF-IMLCZO project, currently NSF-CINet. Variables include: wind speed, carbon flux (NEE), pressure, precipitation, air temperature, relative humidity, 5cm depth soil moisture, 5cm depth soil temperature, solar radiation, ground heat flux. These are raw data, date range of 2016-2020, used in Farahani and Goodwell, 2024 (JGR Biogeosciences, DOI: 10.1029/2023JG007815): Causal Drivers of Land-atmosphere fluxes from machine learning models and data. The associated Github repository (https://github.com/allisongoodwell/Farahani_CarbonML2023) contains code for the outlier removal and pre-processing applied to this data, which were used for machine learning models for carbon flux prediction.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:

WGS 84 EPSG:4326

Coordinate Units:

Decimal degrees

Place/Area Name:

Goose Creek Flux Tower

Longitude

-88.5789°

Latitude

40.1553°

Temporal

Start Date:
End Date:

Content

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README.txt

README

15-minute time-series data from flux tower at Goose Creek site, originally part of the NSF-IMLCZO project, currently NSF-CINet.  Variables include: wind speed, carbon flux (NEE), pressure, precipitation, air temperature, relative humidity, 5cm depth soil moisture, 5cm depth soil temperature, solar radiation, ground heat flux.  

Date range: 2016-2020

These are raw data used in Farahani and Goodwell, 2024 (JGR Biogeosciences, accepted): Causal Drivers of Land-atmosphere fluxes from machine learning models and data.  The associated Github repository:
https://github.com/allisongoodwell/Farahani_CarbonML2023
contains code for the outlier removal and pre-processing applied to this data, which were used for machine learning models for carbon flux prediction.


Description of variables:

NewDate: date

rslt_wnd_spd: wind speed, 25m height, m/s

Fc_li_wpl: net ecosystem exchange, 25m height, umolCO2/m2s

amb_press_li_mean: air pressure, kPa

Precip_Tot: precipitation, mm

T_tmpr_rh_mean: air temperature, C

RH_tmpr_rh_mean: relative humidity, %

D5TE_VWC_5cm_Avg: soil moisture, 5cm depth, %

D5TE_T_5cm_Avg: soil temperature, 5cm depth, C

Rn_Avg: net radiation, W/m2

SQ_110_Avg: ground heat flux, W/m2


Contact for questions:
Allison Goodwell, goodwel2@illinois.edu

Additional Metadata

Name	Value
Associated Paper DOI	10.1029/2023JG007815

Related Resources

Credits

Funding Agencies

This resource was created using funding from the following sources:

Agency Name	Award Title	Award Number
National Science Foundation	Critical Interface Network (CINet) for Intensively Managed Landscapes	EAR-2012850
National Science Foundation	Critical Zone Observatory for Intensively Managed Landscapes	EAR-1331906

Contributors

People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name	Organization	Address	Phone	Author Identifiers
Mozhgan Askarzadehfarahani

How to Cite

Goodwell, A., M. Askarzadehfarahani (2024). Carbon fluxes, meteorological, soil variables from GC Flux Tower, IMLCZO/CINet, HydroShare, https://doi.org/10.4211/hs.1752781278c74d4ba52d986a73070c04

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

http://creativecommons.org/licenses/by/4.0/

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