Geology topics

Eric Ward

Publications and source records attributed to Eric Ward.

25 records · Page 2Linked to original sources

USGS2021

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

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and 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 wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed 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 realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was 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 wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).

Agricultural and Forest Meteorology↗

USGS2021

Restoration of mangrove forest

Mangrove forests occur worldwide along tropical coasts in inundated soils where primary production and anaerobic conditions contribute to the building of soil organic matter (Also see Mangroves Hot-spot, Volume 2). Note that peat may accumulate in certain coastal mangrove (Middleton and McKee, 2001). The actual amount of soil organic matter stored in these wetlands depends on the balance between primary production and decomposition processes (Middleton and McKee, 2001; Kolka et al., 2018; Middleton, 2020). The restoration of mangroves can increase carbon stocks both in soil and aboveground biomass (Wickland et al., 2013; Chimner et al., 2017; Friess et al., 2019). While tropical inland peatland forests may have higher carbon sequestration rates than mangrove swamps, methane (CH4) emissions are generally lower in mangrove swamp, so that these wetlands have greater carbon sequestration potential (Kolka et al., 2018; Al-Haj and Fulweiler, 2020). Mangroves and forested boreal and temperate peatlands tend to store more carbon than non-forested peatlands (Kolka et al., 2018).

Book chapter↗

USGS2020

Throughfall reduction x fertilization: Deep soil water usage in a clay rich ultisol under loblolly pine in the Southeast USA

Forests in the Southeast USA are predicted to experience a moderate decrease in precipitation inputs over this century that may result in soil water deficiency during the growing season. The potential impact of a drier climate on the productivity of managed loblolly pine (Pinus taeda L.) plantations in the Southeast USA is uncertain. Access to water reserves in deep soil during drought periods may help buffer these forests from the effects of water deficits. To better understand the potential impact of drought on deep soil water, we studied the combined effects of throughfall reduction and fertilization on soil water usage in a clay rich Piedmont Ultisol to a depth of 3 m. In a 6-year-old loblolly pine plantation, we applied a throughfall reduction treatment (ambient vs. ~30% throughfall reduction) and a fertilization treatment (no fertilization vs. fertilization). Over 28 months, throughfall reduction lowered soil moisture for all depths and differences were significant in the surface soils (0–0.3 m) (1.2–3.6%) and deep soils (below 2 m) (2.6–3.6%). Fertilization also lowered soil moisture for all depths and differences were significant at 0.3–0.6 m (2.9%) and 1.94–3.06 m (4.5%). Fertilization when combined with the throughfall reduction treatment significantly decreased soil water at 0.1–0.9 m depth. Soils of all depths were rarely depleted of plant available water with the exception of 0–0.1 m, mainly during the growing season. Under throughfall reduction treatment, soil below 0.9 m consistently accounted for more than half of the change in plant available water during months when transpiration exceeded precipitation. When considering the whole soil profile in this clay rich Ultisol, soil water storage buffered transpirational demand in the face of decreasing throughfall input.

Frontiers in Forests and Global Change↗

USGS2019

Using δ13C and δ18O to analyze loblolly pine (Pinus taeda L.) response to experimental drought and fertilization

Drought frequency and intensity are projected to increase throughout the southeastern USA, the natural range of loblolly pine (Pinus taeda L.), and are expected to have major ecological and economic implications. We analyzed the carbon and oxygen isotopic compositions in tree ring cellulose of loblolly pine in a factorial drought (~30% throughfall reduction) and fertilization experiment, supplemented with trunk sap flow, allometry and microclimate data. We then simulated leaf temperature and applied a multi-dimensional sensitivity analysis to interpret the changes in the oxygen isotope data. This analysis found that the observed changes in tree ring cellulose could only be accounted for by inferring a change in the isotopic composition of the source water, indicating that the drought treatment increased the uptake of stored moisture from earlier precipitation events. The drought treatment also increased intrinsic water-use efficiency, but had no effect on growth, indicating that photosynthesis remained relatively unaffected despite 19% decrease in canopy conductance. In contrast, fertilization increased growth, but had no effect on the isotopic composition of tree ring cellulose, indicating that the fertilizer gains in biomass were attributable to greater leaf area and not to changes in leaf-level gas exchange. The multi-dimensional sensitivity analysis explored model behavior under different scenarios, highlighting the importance of explicit consideration of leaf temperature in the oxygen isotope discrimination (Δ18Oc) simulation and is expected to expand the inference space of the Δ18Oc models for plant ecophysiological studies.

Tree Physiology↗

USGS2019

Photosynthetic and respiratory responses of two bog shrub species to whole ecosystem warming and elevated CO2 at the boreal-temperate ecotone

Peatlands within the boreal-temperate ecotone contain the majority of terrestrial carbon in this region, and there is concern over the fate of such carbon stores in the face of global environmental changes. The Spruce and Peatland Response Under Changing Environments (SPRUCE) facility aims to advance the understanding of how such peatlands may respond to such changes, using a combination of whole ecosystem warming (WEW; +0, 2.25, 4.5, 6.75, and 9°C) and elevated CO 2 (eCO 2 ; +500 ppm) treatments in an intact bog ecosystem. We examined photosynthetic and respiration responses in leaves of two ericaceous shrub species–leatherleaf [ Chamaedaphne calyculata (L.) Moench] and bog Labrador tea [ Rhododendron groenlandicum (Oeder) Kron & Judd]–to the first year of combined eCO 2 and WEW treatments at SPRUCE. We surveyed the leaf N content per area ( N area ), net photosynthesis ( A ST ) and respiration ( R D 25 ) at 25°C and 400 ppm CO 2 and net photosynthesis at mean growing conditions ( A GR ) of newly emerged, mature and overwintered leaves. We also measured leaf non-structural carbohydrate content ( NSC ) in mature leaves. The effects of WEW and eCO 2 varied by season and species, highlighting the need to accommodate such variability in modeling this system. In mature leaves, we did not observe a response to either treatment of A ST or R D 25 in R. groenlandicum , but we did observe a 50% decrease in A ST of C. calyculata with eCO 2 . In mature leaves under eCO 2 , neither species had increased A GR and both had increases in NSC , indicating acclimation of photosynthesis to eCO 2 may be related to source-sink imbalances of carbohydrates. Thus, productivity gains of shrubs under eCO 2 may be lower than previously predicted for this site by models not accounting for such acclimation. In newly emerged leaves, A ST increased with WEW in R. groenlandicum , but not C. calyculata . Overwintered leaves exhibited a decrease in R D 25 for R. groenlandicum and in A ST for C. calyculata with increasing WEW, as well as an increase of A GR with eCO 2 in both species. Responses in newly emerged and overwintered leaves may reflect physiological acclimation or phenological changes in response to treatments.

Minnesota↗

USGS2019

FLUXNET-CH4 synthesis activity: Objectives, observations, and future directions

This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH 4 ) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH 4 flux measurements globally, initial results comparing CH 4 fluxes across the sites, and future research directions and needs. Annual estimates of net CH 4 fluxes across sites ranged from −0.2 ± 0.02 g C m –2 yr –1 for an upland forest site to 114.9 ± 13.4 g C m –2 yr –1 for an estuarine freshwater marsh, with fluxes exceeding 40 g C m –2 yr –1 at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH 4 flux across wetland sites globally. Water table position was positively correlated with annual CH 4 emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH 4 fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH 4 estimates due to gap-filling and random errors were on average ±1.6 g C m –2 yr –1 at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH 4 flux database, the controls on ecosystem CH 4 fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH 4 emissions.

Bulletin of the American Meteorological Society↗

USGS2016

Daniel Goodman’s empirical approach to Bayesian statistics

Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

PeerJ↗