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  1. Abstract. Repeated sampling of spatially distributed riverchemistry can be used to assess the location, scale, and persistence ofcarbon and nutrient contributions to watershed exports. Here, we provide acomprehensive set of water chemistry measurements and ecohydrologicalmetrics describing the biogeochemical conditions of permafrost-affectedArctic watersheds. These data were collected in watershed-wide synopticcampaigns in six stream networks across northern Alaska. Three watershedsare associated with the Arctic Long-Term Ecological Research site at ToolikField Station (TFS), which were sampled seasonally each June and August from2016 to 2018. Three watersheds were associated with the National ParkService (NPS) of Alaska and the U.S. Geological Survey (USGS) and weresampled annually from 2015 to 2019. Extensive water chemistrycharacterization included carbon species, dissolved nutrients, and majorions. The objective of the sampling designs and data acquisition was tocharacterize terrestrial–aquatic linkages and processing of material instream networks. The data allow estimation of novel ecohydrological metricsthat describe the dominant location, scale, and overall persistence ofecosystem processes in continuous permafrost. These metrics are (1)subcatchment leverage, (2) variance collapse, and (3) spatial persistence.Raw data are available at the National Park Service Integrated Resource Management Applications portal (O'Donnell et al., 2021, and within the Environmental Data Initiative (Abbott, 2021,
  2. Abstract. Waters impounded behind dams (i.e., reservoirs) areimportant sources of greenhouses gases (GHGs), especially methane (CH4), butemission estimates are not well constrained due to high spatial and temporalvariability, limitations in monitoring methods to characterize hot spot andhot moment emissions, and the limited number of studies that investigatediurnal, seasonal, and interannual patterns in emissions. In this study, weinvestigate the temporal patterns and biophysical drivers of CH4emissions from Acton Lake, a small eutrophic reservoir, using a combinationof methods: eddy covariance monitoring, continuous warm-season ebullitionmeasurements, spatial emission surveys, and measurements of key drivers ofCH4 production and emission. We used an artificial neural network togap fill the eddy covariance time series and to explore the relativeimportance of biophysical drivers on the interannual timescale. We combinedspatial and temporal monitoring information to estimate annualwhole-reservoir emissions. Acton Lake had cumulative areal emission rates of45.6 ± 8.3 and 51.4 ± 4.3 g CH4 m−2 in 2017 and 2018,respectively, or 109 ± 14 and 123 ± 10 Mg CH4 in 2017 and2018 across the whole 2.4 km2 area of the lake. The main differencebetween years was a period of elevated emissions lasting less than 2 weeksin the spring of 2018, which contributed 17 % of the annual emissions inthe shallow region of the reservoir. The spring burst coincided with aphytoplankton bloom, which was likely drivenmore »by favorable precipitation andtemperature conditions in 2018 compared to 2017. Combining spatiallyextensive measurements with temporally continuous monitoring enabled us toquantify aspects of the spatial and temporal variability in CH4emission. We found that the relationships between CH4 emissions andsediment temperature depended on location within the reservoir, and we observed a clearspatiotemporal offset in maximum CH4 emissions as a function ofreservoir depth. These findings suggest a strong spatial pattern in CH4biogeochemistry within this relatively small (2.4 km2) reservoir. Inaddressing the need for a better understanding of GHG emissions fromreservoirs, there is a trade-off in intensive measurements of one water bodyvs. short-term and/or spatially limited measurements in many waterbodies. The insights from multi-year, continuous, spatially extensivestudies like this one can be used to inform both the study design andemission upscaling from spatially or temporally limited results,specifically the importance of trophic status and intra-reservoirvariability in assumptions about upscaling CH4 emissions.« less