Geology topics

Tamlin M. Pavelsky

Publications and source records attributed to Tamlin M. Pavelsky.

5 recordsLinked to original sources

USGS2025

Effects of total suspended solids on photomineralization of dissolved organic matter in the Peace-Athabasca Delta, Canada

Northern deltas receive chromophoric dissolved organic matter (CDOM) from their watersheds, which can be oxidized to carbon dioxide upon absorption of sunlight (i.e., photomineralized). These deltas also receive total suspended solids (TSS), which may shade sunlight absorption by CDOM, thus limiting photomineralization. To quantify this interaction for the first time, we measured photomineralization rates at 11sites in the Peace‐Athabasca Delta (PAD), Canada. We sampled waters during a July 2022 field campaign for TSS concentration, CDOM concentration ( α CDOM ,λ), total downwelling sunlight attenuation coefficients ( K d , tot , λ ), and light attenuation coefficients due to CDOM ( K d , CDOM,λ ). TSS ranged from <1 to 112 mg/L with an average of 19 ± 34 mg/L (mean ± one standard deviation), an order of magnitude lower than TSS reported in rivers entering the PAD earlier in the open water season. α CDOM ,λ at 305 nm ( α CDOM , 305 ) ranged from 23.3 to 65.2 m -1 , K d , CDOM,305 ranged from 26.3 to 74.1 m −1 , and K d , tot,305 ranged from 19.0 to 63.7 m −1 . The ratio of sunlight absorbed by CDOM relative to total sunlight attenuation K d , CDOM,λ/ K d ,tot,λ was inversely correlated with TSS concentration across all wavelengths measured (305–412 nm). TSS thus limited photomineralization rates by shading CDOM from ultraviolet A and visible wavelengths of sunlight, reducing photomineralization rates by up to 56% compared to rates in the absence of TSS or other non-CDOM particles that attenuate sunlight. Results suggest that shifts in delta hydrology that affect TSS concentration likely influence photomineralization rates within TSS-rich northern deltas.

Peace‐Athabasca Delta↗

USGS2023

Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta

Arctic-boreal wetlands, important ecosystems for biodiversity and ecological services, are experiencing hydrological changes including permafrost thaw, earlier snowmelt, and increased wildfire susceptibility. These changes are affecting wetland productivity, species diversity, and biogeochemical cycles. However, given the diverse forms and structures of wetland vegetation communities, traditional wetland maps generated from lower spatial and spectral resolution satellite imagery lack community-level vegetation classification and miss spatially complex patterns. In this study, we built a cloud-based workflow to map wetland vegetation community of the Peace-Athabasca Delta (PAD), Canada, by leveraging high-resolution (5-m) airborne multi-sensor datasets, namely NASA's Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and a historical LiDAR archive. Validation of our classifications using ground references indicates that classifications derived from AVIRIS-NG have higher accuracies (≥87.9%) than either UAVSAR (65.6%) or LiDAR (75.9%) for mapping wetland vegetation communities. We also show improved classification accuracy when combining information from multiple sensors. In particular, incorporating AVIRIS-NG and UAVSAR datasets substantially reduced omission errors of wet graminoid and wet shrub classes from 29.6% to 20.5% and from 10.8% to 7.5%, respectively. Combining AVIRIS-NG and LiDAR datasets further improves overall accuracy (+2.2%) for most classifications, especially emergent vegetation, wet graminoid, and wet shrub. The best performing model, using features derived from all three sensors, achieved an overall accuracy of 93.5%. The framework established here can be used to leverage extensive airborne AVIRIS-NG and UAVSAR datasets collected across Alaska and northwest Canada to understand the spatial distribution of Arctic-Boreal wetland vegetation communities.

Remote Sensing of Environment↗

USGS2022

Hydrologic and landscape controls on dissolved organic matter composition across western North American Arctic lakes

Northern high-latitude lakes are hotspots for cycling dissolved organic carbon (DOC) inputs from allochthonous sources to the atmosphere. However, the spatial distribution of lake dissolved organic matter (DOM) is largely unknown across Arctic-boreal regions with respect to the surrounding landscape. We expand on regional studies of northern high-latitude DOM composition by integrating DOC concentrations, optical properties, and molecular-level characterization from lakes spanning the Canadian Taiga to the Alaskan Tundra. Lakes were sampled during the summer from July to early September to capture the growing season. DOM became more optically processed and molecular-level aromaticity increased northward across the Canadian Shield to the southern Arctic and from interior Alaska to the Tundra, suggesting relatively greater DOM incorporation from allochthonous sources. Using water isotopes (δ 18 O-H 2 O), we report a weak overall trend of increasing DOC and decreasing aromaticity in lakes that were hydrologically isolated from the landscape and enriched in δ 18 O-H 2 O, while within-region trends were stronger and varied depending on the landscape. Finally, DOC correlated weakly with chromophoric dissolved organic matter (CDOM) across the study sites, suggesting that autochthonous and photobleached DOM were a major component of the DOC in these regions; however, some of the northernmost and wetland-dominated lakes followed pan-Arctic riverine DOC-CDOM relationships, indicating strong contributions from allochthonous inputs. As many lakes across the North American Arctic are experiencing changes in temperature and precipitation, we expect the proportions of allochthonous and autochthonous DOM to respond with aquatic optical browning with greater landscape connectivity and more internally produced DOM in hydrologically isolated lakes.

Global Biogeochemical Cycles↗

USGS2022

The importance of lake emergent aquatic vegetation for estimating Arctic-boreal methane emissions

Areas of lakes that support emergent aquatic vegetation emit disproportionately more methane than open water but are under-represented in upscaled estimates of lake greenhouse gas emissions. These shallow areas are typically less than ∼1.5 m deep and can be detected with synthetic aperture radar (SAR). To assess the importance of lake emergent vegetation (LEV) zones to landscape-scale methane emissions, we combine airborne SAR mapping with field measurements of vegetated and open-water methane flux. First, we use Uninhabited Aerial Vehicle SAR data from the NASA Arctic-Boreal Vulnerability Experiment to map LEV in 4,572 lakes across four Arctic-boreal study areas and find it comprises ∼16% of lake area, exceeding previous estimates, and exhibiting strong regional differences (averaging 59 [50–68]%, 22 [20–25]%, 1.0 [0.8–1.2]%, and 7.0 [5.0–12]% of lake areas in the Peace-Athabasca Delta, Yukon Flats, and northern and southern Canadian Shield, respectively). Next, we account for these vegetated areas through a simple upscaling exercise using paired methane fluxes from regions of open water and LEV. After excluding vegetated areas that could be accounted for as wetlands, we find that inclusion of LEV increases overall lake emissions by 21 [18–25]% relative to estimates that do not differentiate lake zones. While LEV zones are proportionately greater in small lakes, this relationship is weak and varies regionally, underscoring the need for methane-relevant remote sensing measurements of lake zones and a consistent criterion for distinguishing wetlands. Finally, Arctic-boreal lake methane upscaling estimates can be improved with more measurements from all lake zones.

Journal of Geophysical Research: Biogeosciences↗

USGS2021

Exploring the factors controlling the error characteristics of the Surface Water and Ocean Topography mission discharge estimates

The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50-100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning-Strickler equation, complementing in-situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters ( e.g. friction coefficient, bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT-like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a “worst case” benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst-case SWOT discharge accuracy.

Water Resources Research↗