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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling study of deep direct use geothermal on the West Virginia university campus-morgantown, WV

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use (DDU) system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. Consequently, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided only the single permeability model is needed; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the prediction, therefore, heterogeneity is included in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis will be used for further economic studies.

15 GEOTHERMAL ENERGY↗
NASA NTRS1988

Probabilistic Structural Analysis Methods for select space propulsion system structural components (PSAM)

The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

Cruse, T. A.↗
NASA NTRS2023

A Sensitive Search for Supernova Emission Associated with the Extremely Energetic and Nearby GRB 221009A

We report observations of the optical counterpart of the long gamma-ray burst GRB 221009A. Due to the extreme rarity of being both nearby (z = 0.151) and highly energetic (E ϒ,iso ≥ 10 54 erg), GRB 221009A offers a unique opportunity to probe the connection between massive star core collapse and relativistic jet formation across a very broad range of γ-ray properties. Adopting a phenomenological power-law model for the afterglow and host galaxy estimates from high-resolution Hubble Space Telescope imaging, we use Bayesian model comparison techniques to determine the likelihood of an associated supernova (SN) contributing excess flux to the optical light curve. Though not conclusive, we find moderate evidence (K Bayes =10 1.2 for the presence of an additional component arising from an associated SN, SN 2022xiw, and find that it must be substantially fainter (<67% as bright at the 99% confidence interval) than SN 1998bw. Given the large and uncertain line-of-sight extinction, we attempt to constrain the SN parameters (M Ni , M ej and E KE ) under several different assumptions with respect to the host galaxy's extinction. We find properties that are broadly consistent with previous GRB-associated SNe: M Ni = 0.05–0.25 M ⊙ , M ej = 3.5–11.1 M ⊙ , and E KE = (1.6–5.2) × 10 52 erg. We note that these properties are weakly constrained due to the faintness of the SN with respect to the afterglow and host emission, but we do find a robust upper limit on M Ni of M Ni < 0.36 M ⊙ . Given the tremendous range in isotropic gamma-ray energy release exhibited by GRBs (seven orders of magnitude), the SN emission appears to be decoupled from the central engine in these systems.

Core-collapse supernovae↗
DOE OSTI2025

Temperature and Water Levels Collectively Regulate Methane Emissions From Subtropical Freshwater Wetlands

Abstract Wetlands are the largest and most climate‐sensitive natural sources of methane. Accurately estimating wetland methane emissions involves reconciling inversion (“top‐down”) and process‐based (“bottom‐up”) models within the global methane budget. However, estimates from these two model types are inherently interdependent and often reveal substantial discrepancies. To enhance the reliability of both approaches, we need a comprehensive understanding of wetland methane emissions and an independent high‐resolution long‐term flux data set. Here, we employed a data‐driven random forest approach to identify key variables influencing methane emissions from subtropical freshwater wetlands in the Southeastern United States. The model‐estimated monthly mean methane fluxes fit well with measured methane fluxes ( R 2 = 0.67) at four representative FLUXNET‐CH4 wetland sites across the region. Variable importance analysis highlighted the sensitivity of subtropical freshwater wetland methane emissions to variations in both temperature and water levels. High temperatures facilitate methanogenesis by enhancing microbial activities, while elevated water levels maintain anaerobic conditions necessary for methane production. Notably, the response of methane emissions to water level fluctuations is contingent on temperature conditions, and vice versa. Moreover, we constructed the first high‐spatial‐resolution (∼1 km × 1 km) and long‐term (1982–2010) gridded regional wetland methane flux product for the Southeastern United States, estimating annual methane emissions from subtropical freshwater wetlands in the region at 4.93 ± 0.11 Tg CH 4 yr −1 for 1982–2010. This new benchmark product holds promise for validating and parameterizing uncertain wetland methane emission processes in bottom‐up models and provides improved prior information for top‐down models.

He, Keqi [Earth and Climate Sciences Nicholas Scho↗
DOE OSTI2024

Association Kinetics for Perfluorinated n -Alkyl Radicals

Radical-radical reaction channels are important in the pyrolysis and oxidation chemistry of perfluoroalkyl substances (PFAS). In particular, unimolecular dissociation reactions within unbranched n-perfluoroalkyl chains, and their corresponding reverse barrierless association reactions, are expected to be significant contributors to the gas-phase thermal decomposition of families of species such as perfluorinated carboxylic acids and perfluorinated sulfonic acids. Unfortunately, experimental data for these reactions are scarce and uncertain. Furthermore, obtaining reliable theoretical predictions for such reactions is a laborious and computationally intensive task. Here, in this work, the chemical kinetics of the various association/decomposition reactions producing/decomposing the C 2 -C 4 series of unbranched n-perfluoroalkanes (C 2 F 6 , C 3 F 8 , and C 4 F 10 ) are examined using state-of-the-art ab initio transition-state-theory-based master-equation calculations. The variable-reaction-coordinate transition-state theory (VRC-TST) formalism is employed in computing the microcanonical and canonical rates for the association reactions. Reaction thermochemistry is obtained via composite quantum chemistry calculations and the laddering of error-canceling reaction schemes via a connectivity-based hierarchy approach employing ANL1/ANL0-style reference energies. Lennard-Jones collision model parameters for the considered systems were estimated by a direct dynamics approach, and collisional energy transfer parameters were obtained from analogies to systems of similar size and heavy-atom connectivity. A one-dimensional master equation approach was used to convert the microcanonical rate coefficients from the VRC-TST analysis into temperature- and pressure-dependent rate constants for the association reactions and the reverse dissociation reactions. The data are reported in standardized formats for usage in comprehensive chemical kinetic models for PFAS thermal destruction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Observations suggest that North African dust absorbs less solar radiation than models estimate

Abstract Desert dust accounts for a large fraction of shortwave radiation absorbed by aerosols, which adds to the climate warming produced by greenhouse gases. However, it remains uncertain exactly how much shortwave radiation dust absorbs. Here, we leverage in-situ measurements of dust single-scattering albedo to constrain absorption at mid-visible wavelength by North African dust, which accounts for approximately half of the global dust. We find that climate and chemical transport models overestimate North African dust absorption aerosol optical depth (AAOD) by up to a factor of two. This occurs primarily because models overestimate the dust imaginary refractive index, the effect of which is partially masked by an underestimation of large dust particles. Similar factors might contribute to an overestimation of AAOD retrieved by the Aerosol Robotic Network, which is commonly used to evaluate climate and chemical transport models. The overestimation of dust absorption by models could lead to substantial biases in simulated dust impacts on the Earth system, including warm biases in dust radiative effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Deep Direct-Use Feasibility Study Numerical Modeling and Uncertainty Analysis using iTOUGH2 for West Virginia University

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. As a result, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided to use single permeability model for further analysis; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the perdition, therefore, is considered in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis are used for economic analysis. The dataset submitted here support the described study. Manuscript is submitted to Geothermics, will be linked once paper is accepted.

15 GEOTHERMAL ENERGY↗
NASA NTRS2022

Global Hotspots of Salt Marsh Change and Carbon Emissions

Salt marshes provide ecosystem services such as carbon sequestration, coastal protection, sea-level-rise (SLR) adaptation and recreation. SLR, storm events, drainage and mangrove encroachment are known drivers of salt marsh loss. However, the global magnitude and location of changes in salt marsh extent remains uncertain. Here we conduct a global and systematic change analysis of Landsat satellite imagery from the years 2000–2019 to quantify the loss, gain and recovery of salt marsh ecosystems and then estimate the impact of these changes on blue carbon stocks. We show a net salt marsh loss globally, equivalent to an area double the size of Singapore (719 km^2), with a loss rate of 0.28% year^(−1) from 2000 to 2019. Net global losses resulted in 16.3 (0.4–33.2, 90% confidence interval) Tg CO_2 e year^(−1) emissions from 2000 to 2019 and a 0.045 (−0.14–0.115) Tg CO_2 e year^(−1) reduction of carbon burial. Russia and the USA accounted for 64% of salt marsh losses, driven by hurricanes and coastal erosion. Our findings highlight the vulnerability of salt marsh systems to climatic changes such as SLR and intensification of storms and cyclones.

Carbon Monitoring↗
NASA NTRS2023

Observations Suggest That North African Dust Absorbs Less Solar Radiation Than Models Estimate

Desert dust accounts for a large fraction of shortwave radiation absorbed by aerosols, which adds to the climate warming produced by greenhouse gases. However, it remains uncertain exactly how much shortwave radiation dust absorbs. Here, we leverage in-situ measurements of dust single-scattering albedo to constrain absorption at mid-visible wavelength by North African dust, which accounts for approximately half of the global dust. We find that climate and chemical transport models overestimate North African dust absorption aerosol optical depth (AAOD) by up to a factor of two. This occurs primarily because models overestimate the dust imaginary refractive index, the effect of which is partially masked by an underestimation of large dust particles. Similar factors might contribute to an overestimation of AAOD retrieved by the Aerosol Robotic Network, which is commonly used to evaluate climate and chemical transport models. The overestimation of dust absorption by models could lead to substantial biases in simulated dust impacts on the Earth system, including warm biases in dust radiative effects.

Atmospheric science↗
DOE OSTI2020

The age distribution of global soil carbon inferred from radiocarbon measurements

Soils contain more carbon than the atmosphere and vegetation combined. An increased flow of carbon from the atmosphere into soil pools could help mitigate anthropogenic emissions of carbon dioxide and climate change. Yet we do not know how quickly soils might respond because the age distribution of soil carbon is uncertain. Here we used 789 radiocarbon (Δ 14 C) profiles, along with other geospatial information, to create globally gridded datasets of mineral soil Δ 14 C and mean age. We found that soil depth is a primary driver of Δ 14 C, whereas climate (for example, mean annual temperature) is a major control on the spatial pattern of Δ 14 C in surface soil. Integrated to a depth of 1m, global soil carbon has a mean age of 4,830 ± 1,730 yr, with older carbon in deeper layers and permafrost regions. In contrast, vertically resolved land models simulate Δ 14 C values that imply younger carbon ages and a more rapid carbon turnover. Overall, our data-derived estimates of older mean soil carbon age suggest that soils will accumulate less carbon than predicted by current Earth system models over the twenty-first century. Reconciling these models with the global distribution of soil radiocarbon will require a better representation of the mechanisms that control carbon persistence in soils.

58 GEOSCIENCES↗
DOE OSTI2025

Modeling of the high column density systems in the Lyman-Alpha forest

The Lyman-α forests observed in the spectra of high-redshift quasars can be used as a tracer of the cosmological matter density to study baryon acoustic oscillations (BAO) and the Alcock-Paczynski effect. Extraction of cosmological information from these studies requires modeling of the forest correlations. While the models depend most importantly on the bias parameters of the intergalactic medium (IGM), they also depend on the numbers and characteristics of high-column-density systems (HCDs) ranging from Lyman-limit systems with column densities log N HI /1cm -2 > 17 to damped Lyman-α systems (DLAs) with log N HI /1cm -2 > 20.2. These HCDs introduce broad damped absorption characteristic of a Voigt profile. Consequently they imprint a component on the power spectrum whose modes in the radial direction are suppressed, leading to a scale-dependent bias. Using mock data sets of known HCD content, we test a model that describes this effect in terms of the distribution of column densities of HCDs, the Fourier transforms of their Voigt profiles and the bias of the halos containing the HCDs. Our results show that this physically well-motivated model describes the effects of HCDs with an accuracy comparable to that of the ad-hoc models used in published forest analyses. We also discuss the problems of applying the model to real data, where the HCD content and their bias is uncertain.

Lyman alpha forest↗
DOE OSTI2021

Thermal Property Characterization of Molten Salt Reactor–Relevant Salts

The thermochemical and thermophysical properties of molten salt systems are necessary input for the design, licensing, and deployment of molten salt reactor concepts. The thermochemical and thermophysical property measurement capabilities being used and developed at Oak Ridge National Laboratory (ORNL) include liquid density, volumetric thermal expansion, viscosity, vapor pressure, phase behavior, and heat capacity and thermal conductivity measurement capabilities. Properties of eutectic LiF-NaF-KF (FLiNaK) and NaCl-KCl are being measured to validate the measurement techniques to be used to provide data to reactor developers and to better quantify the uncertainty of measurements. This report summarizes the status of thermal conductivity, viscosity, specific heat capacity, and density. A method for property estimation using Redlich Kister expansion is also described. This technique will be used to help validate measured properties and to guide future experiments by targeting mixtures or components for which data are uncertain or lacking.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Earth System Reanalysis in Support of Climate Model Improvements

Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial timescales. Further model improvements are expected to arise specifically from improved representation of physical processes realized through model-data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet, the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble and ML methods might be what is required in the future. In this context ML could be important to handle non-linear responses, and to better approximate non-Gaussian distributions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Distributed Solar Adoption in Orlando: A Household-Level Model for Distribution Resource Planning

Potential for rooftop solar in Florida is massive (47% of retail sales, 3rd overall nationally), yet adoption lags (12th nationally). A 2018 Florida Public Service Commission ruling authorizing solar third-party ownership (leasing) has substantially increased attention on distributed solar in the state. The city of Orlando has committed to a 100% clean-energy target by 2050 and deployment of solar and storage are expected to contribute significantly to reaching the goal. Deployment of customer-adopted solar, unlike utility-procured solar, is uncertain, but known to be spatially correlated with demographic factors and existing adoption. We develop a new method to adapt NREL's dGen model in order to represent building-level agents in adoption forecasts for the Orlando Utility Commission (OUC) service territory. Using the agent-based model we develop projections of solar adoption, subject to scenarios varying future solar costs and valuation, and aggregate adoption predictions by OUC distribution feeder. We find substantial spatial heterogeneity in the projected level of adoption by OUC distribution feeder. For instance, 25% of all projected adoption through 2050 would be concentrated on just 5% of feeders and 88% of projected adoption on 50% of feeders. Because of the uncertainty in adoption, bottoms-up solar adoption forecasting methods at the household-level are integral to long-term resource planning by anticipating system needs as customers increasingly adopt distributed solar, storage, electric vehicles, and other distributed energy resources.

14 SOLAR ENERGY↗
DOE OSTI2021

Local and Global Sensitivity Analysis of a Reactive Transport Model Simulating Floodplain Redox Cycling

Reactive transport models (RTMs) are essential tools that simulate the coupling of advective, diffusive, and reactive processes in the subsurface, but their complexity makes them difficult to understand, develop and improve without accompanying statistical analyses. Although global sensitivity analysis (SA) can address these issues, the computational cost associated with most global SA techniques limits their use with RTMs. In this study, we apply distance-based generalized sensitivity analysis (DGSA), a novel and computationally efficient method of global SA, to a floodplain-scale RTM and compare DGSA results to those from local SA. Our test case focuses on the impact of 17 uncertain environmental parameters on spatially and temporally variable redox conditions within a floodplain aquifer. The input parameters considered include flow and diffusion rates, geochemical reaction rates, and the spatial distribution of sediment facies. Sensitivity was evaluated for three distinct components of the model response, encompassing both multidimensional and categorical output. Parameter rankings differ between local SA and DGSA, due to nonlinear effects of individual parameters and interaction effects between parameters. DGSA results show that fluid residence time, which is controlled by aquifer permeability, generally exerts a stronger control on redox conditions than do geochemical reaction rates. Sensitivity indices also demonstrate that sulfate reduction is key for establishing and maintaining reducing conditions throughout the aquifer. Furthermore, these results provide insights into the key drivers of heterogeneous redox processes within floodplain aquifers, as well as the main sources of uncertainty when modeling complex subsurface systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Coupled Lake‐Atmosphere‐Land Physics Uncertainties in a Great Lakes Regional Climate Model

Abstract This study develops a surrogate‐based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and land surface. Perturbed physics ensembles of the model during the 2018 summer are used to train a neural network surrogate model to predict lake surface temperature (LST) and near‐surface air temperature (T2m). Average physics uncertainties are determined to be 1.5C for LST and T2m over land, and 1.9C for T2m over lake, but these have significant spatiotemporal variations. We find that atmospheric physics parameterizations alone are the dominant sources of uncertainty (45%–53%), while lake and land parameterizations account for 33% and 38% of the uncertainty of LST and T2m over land respectively. Interactions of atmosphere physics parameterizations with those of the land and lake contribute to an additional 13%–17% of the total variance. LST and T2m over the lake are more uncertain in the deeper northern lakes, particularly during the rapid warming phase that occurs in late spring/early summer. The LST uncertainty increases with sensitivity to the lake model's surface wind stress scheme. T2m over land is more uncertain over forested areas in the north, where it is most sensitive to the land surface model, than the more agricultural land in the south, where it is most sensitive to the atmospheric planetary boundary and surface layer scheme. Uncertainty also increases in the southwest during multiday temperature declines with higher sensitivity to the land surface model.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America

Fires and the aerosols that they emit impact air quality, health, and climate, but the abundance and properties of carbonaceous aerosol (both black carbon and organic carbon) from biomass burning (BB) remain uncertain and poorly constrained. We aim to explore the uncertainties associated with fire emissions and their air quality and radiative impacts from underlying dry matter consumed and emissions factors. To investigate this, we compare model simulations from a global chemical transport model, GEOS-Chem, driven by a variety of fire emission inventories with surface and airborne observations of black carbon (BC) and organic aerosol (OA) concentrations and satellite-derived aerosol optical depth (AOD). We focus on two fire-detection-based and/or burned-area-based (FD-BA) inventories using burned area and active fire counts, respectively, i.e., the Global Fire Emissions Database version 4 (GFED4s) with small fires and the Fire INventory from NCAR version 1.5 (FINN1.5), and two fire radiative power (FRP)-based approaches, i.e., the Quick Fire Emission Dataset version 2.4 (QFED2.4) and the Global Fire Assimilation System version 1.2 (GFAS1.2). We show that, across the inventories, emissions of BB aerosol (BBA) differ by a factor of 4 to 7 over North America and that dry matter differences, not emissions factors, drive this spread. We find that simulations driven by QFED2.4 generally overestimate BC and, to a lesser extent, OA concentrations observations from two fire-influenced aircraft campaigns in North America (ARCTAS and DC3) and from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, while simulations driven by FINN1.5 substantially underestimate concentrations. The GFED4s and GFAS1.2-driven simulations provide the best agreement with OA and BC mass concentrations at the surface (IMPROVE), BC observed aloft (DC3 and ARCTAS), and AOD observed by MODIS over North America. We also show that a sensitivity simulation including an enhanced source of secondary organic aerosol (SOA) from fires, based on the NOAA Fire Lab 2016 experiments, produces substantial additional OA; however, the spread in the primary emissions estimates implies that this magnitude of SOA can be neither confirmed nor ruled out when comparing the simulations against the observations explored here. Given the substantial uncertainty in fire emissions, as represented by these four emission inventories, we find a sizeable range in 2012 annual BBA PM2.5 population-weighted exposure over Canada and the contiguous US (0.5 to 1.6 µg/cu. m). We also show that the range in the estimated global direct radiative effect of carbonaceous aerosol from fires (−0.11 to −0.048 W/sq. m) is large and comparable to the direct radiative forcing of OA (−0.09 W/sq. m) estimated in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). Our analysis suggests that fire emissions uncertainty challenges our ability to accurately characterize the impact of smoke on air quality and climate.

North America↗
NASA NTRS2023

Extensive Global Wetland Loss Over the Past Three Centuries

Wetlands have long been drained for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity. Nevertheless, the global extent of natural wetland loss remains remarkably uncertain. Here, we reconstruct the spatial distribution and timing of wetland loss through conversion to seven human land uses between 1700 and 2020, by combining national and subnational records of drainage and conversion with land-use maps and simulated wetland extents. We estimate that 3.4 million km 2 (confidence interval 2.9–3.8) of inland wetlands have been lost since 1700, primarily for conversion to croplands. This net loss of 21% (confidence interval 16–23%) of global wetland area is lower than that suggested previously by extrapolations of data disproportionately from high-loss regions. Wetland loss has been concentrated in Europe, the United States and China, and rapidly expanded during the mid-twentieth century. Our reconstruction elucidates the timing and land-use drivers of global wetland losses, providing an improved historical baseline to guide assessment of wetland loss impact on Earth system processes, conservation planning to protect remaining wetlands and prioritization of sites for wetland restoration.

Etienne Fluet-Chouinard↗