scholarly journals Seasonal and spatial variability in dissolved organic matter quantity and composition from the Yukon River basin, Alaska

2008 ◽  
Vol 22 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Robert G. M. Spencer ◽  
George R. Aiken ◽  
Kimberly P. Wickland ◽  
Robert G. Striegl ◽  
Peter J. Hernes
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Spatial Variability ◽  
River Basin ◽  
Yukon River ◽  
Yukon River Basin

scholarly journals Spatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin

2014 ◽  
Vol 119 (6) ◽  
pp. 1244-1265 ◽  
Author(s):  
Neal J. Pastick ◽  
M. Torre Jorgenson ◽  
Bruce K. Wylie ◽  
Joshua R. Rose ◽  
Matthew Rigge ◽  
...  
Keyword(s):  
Spatial Variability ◽  
River Basin ◽  
Near Surface ◽  
Yukon River ◽  
Yukon River Basin

scholarly journals DOM degradation by light and microbes along the Yukon River-coastal ocean continuum

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brice K. Grunert ◽  
Maria Tzortziou ◽  
Patrick Neale ◽  
Alana Menendez ◽  
Peter Hernes
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Salinity Gradient ◽  
Coastal Ocean ◽  
Interactive Effects ◽  
The Arctic ◽  
Yukon River ◽  
Spring Freshet ◽  
Dom Composition

AbstractThe Arctic is experiencing rapid warming, resulting in fundamental shifts in hydrologic connectivity and carbon cycling. Dissolved organic matter (DOM) is a significant component of the Arctic and global carbon cycle, and significant perturbations to DOM cycling are expected with Arctic warming. The impact of photochemical and microbial degradation, and their interactive effects, on DOM composition and remineralization have been documented in Arctic soils and rivers. However, the role of microbes, sunlight and their interactions on Arctic DOM alteration and remineralization in the coastal ocean has not been considered, particularly during the spring freshet when DOM loads are high, photoexposure can be quite limited and residence time within river networks is low. Here, we collected DOM samples along a salinity gradient in the Yukon River delta, plume and coastal ocean during peak river discharge immediately after spring freshet and explored the role of UV exposure, microbial transformations and interactive effects on DOM quantity and composition. Our results show: (1) photochemical alteration of DOM significantly shifts processing pathways of terrestrial DOM, including increasing relative humification of DOM by microbes by > 10%; (2) microbes produce humic-like material that is not optically distinguishable from terrestrial humics; and (3) size-fractionation of the microbial community indicates a size-dependent role for DOM remineralization and humification of DOM observed through modeled PARAFAC components of fluorescent DOM, either through direct or community effects. Field observations indicate apparent conservative mixing along the salinity gradient; however, changing photochemical and microbial alteration of DOM with increasing salinity indicate changing DOM composition likely due to microbial activity. Finally, our findings show potential for rapid transformation of DOM in the coastal ocean from photochemical and microbial alteration, with microbes responsible for the majority of dissolved organic matter remineralization.

Mercury Export from the Yukon River Basin and Potential Response to a Changing Climate

2011 ◽  
Vol 45 (21) ◽  
pp. 9262-9267 ◽  
Author(s):  
Paul F. Schuster ◽  
Robert G. Striegl ◽  
George R. Aiken ◽  
David P. Krabbenhoft ◽  
John F. Dewild ◽  
...  
Keyword(s):  
River Basin ◽  
Potential Response ◽  
Changing Climate ◽  
Yukon River ◽  
Yukon River Basin

A novel application of an adaptive surrogate-based modeling optimization (ASMO) for the Community Territory System Model (CTSM) in Alaska

2021 ◽  
Author(s):  
Yifan Cheng ◽  
Andrew Newman ◽  
Sean Swenson ◽  
David Lawrence ◽  
Anthony Craig ◽  
...  
Keyword(s):  
High Resolution ◽  
River Basin ◽  
Land Surface ◽  
Computational Cost ◽  
Reanalysis Data ◽  
Freshwater Ecosystems ◽  
System Model ◽  
Surface Model ◽  
Yukon River ◽  
Yukon River Basin

<p>Climate-induced changes in snow cover, river flow, and freshwater ecosystems will greatly affect the indigenous groups in the Alaska and Yukon River Basin. To support policy-making on climate adaptation and mitigation for these underrepresented groups, an ongoing interdisciplinary effort is being made to combine Indigenous Knowledge with western science (https://www.colorado.edu/research/arctic-rivers/).</p><p>A foundational component of this project is a high fidelity representation of the aforementioned land surface processes. To this end, we aim to obtain a set of reliable high-resolution parameters for the Community Territory System Model (CTSM) for the continental scale domain of Alaska and the entire Yukon River Basin, which will be used in climate change simulations. CTSM is a complex, physically based state-of-the-science land surface model that includes complex vegetation and canopy representation, a multi-layer snow model, as well as hydrology and frozen soil physics necessary for the representation of streamflow and permafrost. Two modifications to the default CTSM configuration were made. First, we used CTSM that is implemented with hillslope hydrology to better capture the fine-scale hydrologic spatial heterogeneity in complex terrain. Second, we updated the input soil textures and organic carbon in CTSM using the high-resolution SoilGrid dataset.</p><p>In this study, we performed a multi-objective optimization on snow and streamflow metrics using an adaptive surrogate-based modeling optimization (ASMO). ASMO permits optimization of complex land-surface models over large domains through the use of surrogate models to minimize the computational cost of running the full model for every parameter combination. We ran CTSM at a spatial resolution of 1/24<sup>th</sup> degree and a temporal resolution of one hour using the ERA5 reanalysis data as the meteorological forcings. The ERA5 reanalysis data were bias-corrected to account for the orographic effects. We will discuss the ASMO-CTSM coupling workflow, performance characteristics of the optimization (e.g., computational cost, iterations), and comparisons of the default configuration and optimized model performance.</p>

Sign in / Sign up

Export Citation Format

Share Document