Suspended sediment and carbonate transport in the Yukon River Basin, Alaska: Fluxes and potential future responses to climate change

2009 ◽  
Vol 45 (6) ◽  
Author(s):  
Mark M. Dornblaser ◽  
Robert G. Striegl
Keyword(s):  
Climate Change ◽  
Suspended Sediment ◽  
River Basin ◽  
Yukon River ◽  
Yukon River Basin

scholarly journals Effects of Disturbance and Climate Change on Ecosystem Performance in the Yukon River Basin Boreal Forest

2014 ◽  
Vol 6 (10) ◽  
pp. 9145-9169 ◽  
Author(s):  
Bruce Wylie ◽  
Matthew Rigge ◽  
Brian Brisco ◽  
Kevin Murnaghan ◽  
Jennifer Rover ◽  
...  
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
River Basin ◽  
Yukon River ◽  
Yukon River Basin

Indigenous Observations of Climate Change in the Lower Yukon River Basin, Alaska

2011 ◽  
Vol 70 (3) ◽  
pp. 244-252 ◽  
Author(s):  
Nicole Herman-Mercer ◽  
Paul Schuster ◽  
Karonhiakt'tie Maracle
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Yukon River ◽  
Yukon River Basin

Hydrologic effects of climate change in the Yukon River Basin

2010 ◽  
Vol 100 (3-4) ◽  
pp. 509-523 ◽  
Author(s):  
Lauren E. Hay ◽  
Gregory J. McCabe
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Yukon River ◽  
Yukon River Basin

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>

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