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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The Role of Atmospheric Rivers on Groundwater: Lessons Learned From an Extreme Wet Year

Abstract In the coastal regions of the western United States, atmospheric rivers (ARs) are associated with the largest precipitation generating storms and contribute up to half of annual precipitation, but the impact of ARs on the integrated hydrologic cycle, specifically on groundwater storage and hydrodynamics, is largely unknown. To better explore the hydrologic behavior of AR versus non‐AR event precipitation, we present a novel combination of two water tracking methods (one in the atmosphere and one in the subsurface) to explicitly track the full lifecycle of water parcels generated by ARs. Simulations of northern California's Cosumnes River watershed during the record wet 2017 water year are performed via the coupling of a high‐resolution regional climate model and a land surface‐groundwater model accounting for lateral groundwater flow. Despite ARs contributing more precipitation than non‐AR storms, we find less AR water is preferentially stored in aquifers by year end. Fractionally, ARs result in 300% less snow derived groundwater‐recharged compared to non‐AR precipitation. Rain‐on‐snow (RoS) plays an important role in AR‐driven discharge, where over 50% of total discharge from ARs snow is from RoS events. Finally, despite record‐breaking annual precipitation, simulated groundwater depletion occurs by year end due to estimates of groundwater pumping activities. The results from these simulations serve as a partial analogue of future hydrologic conditions where ARs are expected to intensify and provide a greater fraction of annual precipitation due to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Upscaling Wetland Methane Emissions From the FLUXNET–CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%–31% of global methane (CH 4 ) emissions and account for a large source of uncertainty in the global CH 4 budget. Data-driven upscaling of CH 4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH 4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH 4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH 4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ~0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH 4 emissions of 146 ± 43 TgCH 4 y –1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH 4 y –1 ) and overlaps with top-down atmospheric inversion models (155–200 TgCH 4 y –1 ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH 4 fluxes has the potential to produce realistic extra-tropical wetland CH 4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ground Heat Flux Reconstruction Using Bayesian Uncertainty Quantification Machinery and Surrogate Modeling

Ground heat flux (G 0 ) is a key component of the land-surface energy balance of high-latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G 0 is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G 0 across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G 0 data (measured or modeled) for snow-free period as a reference. When observed G 0 is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state-of-the-art uncertainty quantification methods, the developed G 0 reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A New Post‐Hoc Method to Reduce the Energy Imbalance in Eddy Covariance Measurements

Abstract Latent and sensible heat flux observations are essential for understanding land–atmosphere interactions. Measurements from the eddy covariance technique are widely used but suffer from systematic energy imbalance problems, partly due to missing large eddies from sub‐mesoscale transport. Because available energy drives the development of large eddies, we propose an available energy based correction method for turbulent flux measurements. We apply our method to 172 flux tower sites and show that we can reduce the energy imbalance from −14.99 to −0.65 W m −2 on average, together with improved consistency between turbulent fluxes and available energy and associated increases inr 2 at individual sites and across networks. Our results suggest that our method is conceptually and empirically preferable over the method implemented in the ONEFlux processing. This can contribute to the efforts in understanding and addressing the energy imbalance issue, which is crucial for the evaluation and calibration of land surface models.

Geology↗
DOE OSTI2023

Carbon and Water Fluxes of the Boreal Evergreen Needleleaf Forest Biome Constrained by Assimilating Ecosystem Carbonyl Sulfide Flux Observations

Gross primary production (GPP) by boreal forests is highly sensitive to environmental changes. However, GPP simulated by land surface models (LSMs) remains highly uncertain due to the lack of direct photosynthesis observations at large scales. Carbonyl sulfide (COS) has emerged as a promising proxy to improve the representation of GPP in LSMs. Because COS is absorbed by vegetation following the same diffusion pathway as CO2 during photosynthesis and not emitted back to the atmosphere, incorporating a mechanistic representation of vegetation COS uptake in LSMs allows using COS observations to refine GPP representation. Here, we perform ecosystem COS flux and GPP data assimilations to constrain the COS- and GPP-related parameters in the ORCHIDEE LSM for boreal evergreen needleleaf forests (BorENF). Assimilating ecosystem COS fluxes at Hyytiälä forest increases the simulated net ecosystem COS uptake by 14%. This increase largely results from changes in the internal conductance to COS, highlighting the need to improve the representation of COS internal diffusion and consumption. Moreover, joint assimilation of ecosystem COS flux and GPP at Hyytiälä improves the simulated latent heat flux, contrary to the GPP-only data assimilation, which fails to do so. Finally, we scaled this assimilation framework up to the boreal region and find that the joint assimilation of COS at Hyytiälä and GPP fluxes at 10 BorENF sites increases the modeled vegetation COS uptake up to 18%, but not GPP. Therefore, this study encourages the use of COS flux observations to inform GPP and latent heat flux representations in LSMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Toward Improved Regional Hydrological Model Performance Using State-Of-The-Science Data-Informed Soil Parameters

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. Our results show WRF-Hydro soil moisture exhibits increased correlation coefficients ( r ), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well-established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-adjusted WRF-Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO-informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre-calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Role of Vapor Pressure Deficit in the CLM Simulated Interaction Between Urban Heat Islands and Heat Waves Over CONUS

Abstract Although both urban and rural temperatures are expected to increase under heat waves (HWs), whether the urban heat island (UHI) intensity becomes stronger under HWs remains unknown especially at the daily mean and large spatial scales. Using an urbanized land surface model, we quantify the interactions between UHIs and HWs over the Contiguous United States (CONUS). Synergistic interactions (i.e., increased UHI intensities under HWs) are observed over the eastern and western U.S. However, negative interactions are found in the Central U.S. due to the stronger inhibition of rural evapotranspiration by vapor pressure deficit (VPD) stresses. The interactions between UHIs and HWs in the Central U.S. will be further reduced along with the elevated VPD stresses in a hotter future. The results highlight the importance of properly parameterizing the sensitivity of urban and rural evapotranspiration to various environmental stresses in climate and earth system models.

Wang, Linying [National Key Laboratory of Earth Sy↗
DOE OSTI2025

Seasonality and Albedo Dependence of Cloud Radiative Forcing in the Upper Colorado River Basin

Mountains create and enhance their own clouds, which both scatter and absorb shortwave radiation from the sun and absorb and re-emit land surface and atmospheric longwave radiation. However, the impacts of clouds on the surface radiation balance in high elevation snowy mountain terrain are poorly explored. In this study, we use data collected by the SAIL field campaign and partner organizations in the upper elevations (2,880 m.a.s.l) of the Upper Colorado River Basin (UCRB) over a 21-month period from September 2021 to June 2023 to estimate Cloud Radiative Forcing (CRF) in the shortwave, longwave, and the net effect. Longwave warming effects dominate during the winter when snow albedos are high (0.8–0.9) and the background atmospheric precipitable water vapor is low (<0.5 cm), yielding a maximum monthly average net CRF of +34.7 W·m -2 , meaning that clouds increase the net radiation relative to clear skies during this time period. The sign of net CRF switches in the warm season as snow recedes, sun-angles increase, and the North American monsoon arrives, yielding a minimum monthly average net CRF of -47.6 W·m -2 with hourly minima of -600 W·m -2 . The sign of net CRF is typically positive, even at solar noon, when the surface is snow covered, except for a brief period over melting, low-albedo snow (0.5–0.6) impacted by dust impurities. Sensitivity tests elucidate the role of the surface albedo on the net CRF. The results suggest that net CRF will increase in magnitude and lead to a more persistent cooling effect on the surface net radiation budget as the snow cover declines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ELM–Wet: Inclusion of a Wet–Landunit With Sub–Grid Representation of Eco–Hydrological Patches and Hydrological Forcing Improves Methane Emission Estimations in the E3SM Land Model (ELM)

Wetlands are the largest emitters of biogenic methane (CH 4 ) and represent the highest source of uncertainty in global CH 4 budgets. Here, we aim to improve the realism of wetland representation in the U.S. Department of Energy's Exascale Earth System Model land surface model, ELM, thereby reducing uncertainty of CH 4 flux predictions. We develop an updated version, ELM-Wet, where we activate a separate landunit for wetlands that handles multiple wetland-specific eco-hydrological patch functional types. We introduce more realistic hydrological forcing through prescribing site-level constraints on surface water elevation, which allows resolving different sustained inundation depth for different patches, and if data exists, prescribing inundation depth. We modified the calculation of aerenchyma transport diffusivity based on observed conductance per leaf area for different vegetation types. We use Bayesian Optimization to parameterize CO 2 and CH 4 fluxes in the developed wet-landunit. Site-level simulations of a coastal non-tidal freshwater wetland in Louisiana were performed with the updated model. Eddy covariance observations of CO 2 and CH 4 fluxes from 2012 to 2013 were used to train the model and data from 2021 were used for validation. Patch-specific chamber flux observations and observations of CH 4 concentration profiles in the soil porewater from 2021 were used for evaluation of the model performance. Our results show that ELM-Wet reduces the model's CH 4 emission root mean squared error by up to 33% and is able to represent inter-daily CO 2 and CH 4 flux variability across the wetland's eco-hydrological patches, including during periods of extreme dry or wet conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2026

The Amazon River‐Breeze Circulation Limits Detection of Aerosol‐Cloud Interactions in Warm Clouds

Increased aerosol concentrations can brighten low-level clouds and extend their lifetimes, but aerosol–cloud interactions (ACI) remain highly uncertain and difficult to quantify. We show that part of this uncertainty is caused by topographical influences on clouds, that is, those arising from land–water contrasts. This is demonstrated using satellite retrievals in regions with extensive river networks, such as the Amazon Basin. 15 years of MODerate resolution Imaging Spectroradiometer (MODIS) satellite data show cloud formation over the Amazon River basin is suppressed by 26% with warm low clouds above the river exhibiting a 22% smaller droplet effective radius and 18% higher droplet concentration ($N_d$) compared to adjacent land clouds. Thus, clouds above the river may appear polluted but are actually influenced by river-breeze circulations driven by the thermal contrast between the river and the surrounding land. These responses are robust in both wet and dry seasons, and tests using an improved MODIS retrieval product show cloud differences are unlikely due to retrieval artifacts. In situ measurements from the Green Ocean Amazon Experiment (GoAmazon) confirm that $N_d$ is elevated above rivers and are also higher when carbon monoxide concentrations are elevated near the large city of Manaus. Lagrangian airmass tracking over Manaus shows that regional-scale river-breeze circulations impact $N_d$ as much as the urban aerosol plume, complicating ACI attribution and highlighting the need to isolate land-surface effects to assess ACI in continental regions.

Aerosol-Cloud Interactions↗
DOE OSTI2026

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗
DOE OSTI2020

Accelerated dryland expansion regulates future variability in dryland gross primary production

Drylands cover 41% of the Earth’s land surface and are the largest source of interannual variability in the global carbon sink. While drylands are projected to experience an accelerated expansion over the next century, the implications of this expansion and degradation for variability in their gross primary production (GPP) remain elusive. Here we show that total dryland GPP by 2100 will increase by 12 ± 3% relative to the 2010–2014 baseline. Because drylands will largely expand into formerly more productive ecosystems, this increase in dryland GPP may not increase total global GPP. Further, GPP per unit dryland area will decrease as degradation of historical drylands outpaces the higher GPP of expanded drylands. Dryland expansion and climate-induced conversions among sub-humid, semi-arid, arid, and hyper-arid subtypes will lead to substantial changes in regional and subtype contributions to global dryland GPP variability. Our results highlight the vulnerability of dryland subtypes to more frequent and severe climate extremes and suggest that strong regional variations will require different mitigation strategies in different regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Mapping the global distribution of C 4 vegetation using observations and optimality theory

Plants with the C 4 photosynthesis pathway typically respond to climate change differently from more common C 3 -type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C 4 and C 3 vegetation distributions. However, current C 4 vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C 4 vegetation. We find that global C 4 vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C 4 natural grass cover due to elevated CO 2 favoring C 3 -type photosynthesis, and an increase in C 4 crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C 4 vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C 4 plants in the contemporary global carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Observed changes in dry-season water availability attributed to human-induced climate change

Human-induced climate change impacts the hydrological cycle and thus the availability of water resources. However, previous assessments of observed warming-induced changes in dryness have not excluded natural climate variability and show conflicting results due to uncertainties in our understanding of the response of evapotranspiration. Here in this paper, we employ data-driven and land-surface models to produce observation-based global reconstructions of water availability from 1902 to 2014, a period during which our planet experienced a global warming of approximately 1°C. Our analysis reveals a spatial pattern of changes in average water availability during the driest month of the year over the past three decades compared with the first half of the twentieth century, with some regions experiencing increased and some decreased water availability. The global pattern is consistent with climate model estimates that account for anthropogenic effects, and it is not expected from natural climate variability, supporting human-induced climate change as the cause. There is regional evidence of drier dry seasons predominantly in extratropical latitudes and including Europe, western North America, northern Asia, southern South America, Australia and eastern Africa. We also find that the intensification of the dry season is generally a consequence of increasing evapotranspiration rather than decreasing precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Confronting the water potential information gap

Water potential directly controls the function of leaves, roots and microbes, and gradients in water potential drive water flows throughout the soil–plant–atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land-surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. Here, we end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely sensed proxies. Together, these considerations offer a road map for clearer links between ecohydrological processes and the water potential gradients that have the ‘potential’ to substantially reduce conceptual and modelling uncertainties.

58 GEOSCIENCES↗
DOE OSTI2026

Emergent constraints on future methane emissions from global wetlands

Future methane (CH 4 ) emissions from natural wetlands are predicted to increase due to global warming, leading to positive feedback on climate change. However, the magnitude of this increase remains highly uncertain. Here we present novel ensemble simulations of seven state-of-the-art terrestrial biosphere models to estimate wetland CH 4 emissions (eCH 4 ) during the twenty-first century. Our estimates suggest that for every 1 °C increase in global land surface temperature, there is a 24 ± 10 Tg CH 4 yr −1 increase in eCH 4 . We also identify an emergent relationship between contemporary temperature dependence and projected eCH 4 . When constrained by 163 site-year eddy-covariance measurements of eCH 4 , we show that wetland emissions can increase by 50–60% by the 2090s relative to the 2010s under a high-warming scenario. The projected decadal increase in eCH 4 from the 2010–2019 baseline to the 2030s would very likely (90% probability) offset an amount equivalent in scale to 8–10% of anthropogenic eCH 4 at the 2020 level, comparable to the reductions committed under the Global Methane Pledge. However, the constraint is dominated by mid- and high-latitude observations, with limited tropical coverage, and uncertainties in projected wetland inundation contribute substantially to uncertainty in eCH 4 . Our findings reduce the uncertainty in projected wetland methane–climate feedback and highlight its potential impacts on methane mitigation efforts to slow global warming.

Zhang, Zhen [Chinese Academy of Sciences (CAS), Be↗
DOE OSTI2020

High-resolution mapping of spatial heterogeneity in ice wedge polygon geomorphology near Prudhoe Bay, Alaska

It is well known that microtopography associated with ice wedge polygons drives pronounced, meter-scale spatial gradients in hydrologic and ecological processes on the tundra. However, highresolution maps of polygonal geomorphology are rarely available, due to the complexity and subtlety of ice wedge polygon relief at landscape scales. Here we present a sub-meter resolution map of >106 discrete ice wedge polygons across a ~1200 km2 landscape, delineated within a lidar-derived digital elevation model. The delineation procedure relies on a convolutional neural network paired with a set of common image processing operations and permits explicit measurement of relative elevation at the center of each ice wedge polygon. The resulting map visualizes meter- to kilometer-scale spatial gradients in polygonal geomorphology across an extensive landscape with unprecedented detail. This high-resolution inventory of polygonal geomorphology provides rich spatial context for extrapolating observations of environmental processes across the landscape. The map also represents an extensive baseline dataset for quantifying contemporary land surface deformation (i.e., thermokarst) at the survey area, through future topographic surveys.

54 ENVIRONMENTAL SCIENCES↗