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Sturtevant, Cove

Publications and source records attributed to Sturtevant, Cove.

10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

AmeriFlux FLUXNET-1F US-Tw4 Twitchell East End Wetland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Tw4 Twitchell East End Wetland. This is the FLUXNET version of the carbon flux data for the site US-Tw4 Twitchell East End Wetland produced by applying the standard ONEFlux (1F) software. Site Description - The Twitchell East End Wetland is a newly constructed restored wetland on Twitchell Island, CA. This site and the surrounding region are part of the San Joaquin - Sacramento River Delta drained beginning in the 1850's and subsequently used for agriculture. The site was previously a corn field. The wetland was designed to have a mix of vegetated and open water channels and ponds (due to surface elevation differences). Flooding of the wetland was done gradually beginning in January, 2014. Berms wind throughout the wetland to allow vehicle access. Tule and Cattail plant material from a nearby wetland were spread along the berms immediately prior to flooding to facilitate plant establishment and stabilization of the berms from wind/water erosion. The tower was installed on November 25, 2013.

Eichelmann, Elke↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-Tw2 Twitchell Corn

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Tw2 Twitchell Corn. This is the FLUXNET version of the carbon flux data for the site US-Tw2 Twitchell Corn produced by applying the standard ONEFlux (1F) software. Site Description - The Twitchell Corn site is a corn field on peat soil. The tower was installed on May 17, 2012 and was equipped to analyze energy, H2O and CO2 fluxes. The field was planted in early May 2012 and harvested in early November 2012. The field was fallow during the non-growing season. The variety of corn used was ES-7477 hybrid corn commercialized by Eureka seeds. Beginning 2013-05-09 this site was converted to wetland. The tower was moved 540m ESE to avoid earth moving in the construction. The site is near US-Tw1, US-Tw3 and US-Twt sites.

Sturtevant, Cove↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-Tw3 Twitchell Alfalfa

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Tw3 Twitchell Alfalfa. This is the FLUXNET version of the carbon flux data for the site US-Tw3 Twitchell Alfalfa produced by applying the standard ONEFlux (1F) software. Site Description - The Twitchell Alfalfa site is an alfalfa field owned by the state of California and leased to third parties for farming. The tower was installed on May 24, 2013. This site and the surrounding region are part of the San Joaquin - Sacramento River Delta drained beginning in the 1850's and subsequently used for agriculture. The field has been alfalfa for X years…., Crop rotation occurs every 5-6 years. The site is harvested by mowing and bailing several times per year. The field is fallow typically between November and February. The site is irrigated by periodically-flooded ditches surrounding the field. The site is irrigated by raising, and subsequently lowering the water table??

Chamberlain, Samuel D↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-Myb Mayberry Wetland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Myb Mayberry Wetland. This is the FLUXNET version of the carbon flux data for the site US-Myb Mayberry Wetland produced by applying the standard ONEFlux (1F) software. Site Description - The Mayberry Wetland site is a 300-acre restored wetland on Sherman Island, north of Mayberry Slough, that is on the property of Mayberry Farms and managed by the California Department of Water Resources and Ducks Unlimited. During Summer 2010, the site was restored from a pepperweed and annual grassland pasture to a wetland through a project managed by Bryan Brock (bpbrock@water.ca.gov). A flux tower equipped to analyze energy, H2O, CO2, and CH4 fluxes was installed on October 14, 2010. At the time of installation, flooding of the site had only recently begun after extensive reconstruction of the wetland bathymetry conducted during the summer. Although some small patches of tules remain within the site, the site is a patchwork of deep and shallow open water with some remaining vegetation. Currently, there is an intention to flood-to-kill the current pepperweed and upland grasses and let the wetland plants propagate naturally, so no additional plant manipulation will occur.

Matthes, Jaclyn Hatala↗
DOE OSTI2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of methane fluxes measured by eddy-covariance require gap-filling to estimate annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for methane, with regards both to the best model algorithms and predictors. In this study, we address the need for standardization by synthesizing results of gap-filling methods applied at 17 wetland sites spanning boreal to tropical regions including all major wetlands classes and two rice paddies. We introduce new procedures for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with robust uncertainty estimates. We tested a conventional method (marginal distribution sampling) and four machine learning algorithms - penalized linear regression, artificial neural networks, random forests, and boosted decision trees - and four predictor sets, including temporal, meteorological, ecosystem carbon and energy flux, and soil predictors. We find that the conventional method can achieve similar median performance to the machine learning models but is worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. Finally, we gap-fill and provide summary evaluation metrics for all 81 sites in the FLUXNET-CH4 community dataset and publicly release the python code for model development, evaluation, and uncertainty estimation.

42 ENGINEERING↗
DOE OSTI2021

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH 4 ) to the atmosphere, they represent a large source of uncertainty in the global CH 4 budget due to the complex biogeochemical controls on CH 4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH 4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. In this work we used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH 4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH 4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH 4 emissions.

59 BASIC BIOLOGICAL SCIENCES↗