Modeling of Future Streamflow Hazards in Interior Alaska River Systems and Implications for Applied Planning

Author:

Bennett Alec P.1ORCID,Alexeev Vladimir A.2ORCID,Bieniek Peter A.2

Affiliation:

1. College of Business & Security Management, University of Alaska Fairbanks, Fairbanks, AK 99775, USA

2. International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK 99775, USA

Abstract

There is a growing need for proactive planning for natural hazards in a changing climate. Computational modeling of climate hazards provides an opportunity to inform planning, particularly in areas approaching ecosystem state changes, such as Interior Alaska, where future hazards are expected to differ significantly from historical events in frequency and severity. This paper considers improved modeling approaches from a physical process perspective and contextualizes the results within the complexities and limitations of hazard planning efforts and management concerns. Therefore, the aim is not only to improve the understanding of potential climate impacts on streamflow within this region but also to further explore the steps needed to evaluate local-scale hazards from global drivers and the potential challenges that may be present. This study used dynamically downscaled climate forcing data from ERA-Interim reanalysis datasets and projected climate scenarios from two General Circulation Models under a single Representative Concentration Pathway (RCP 8.5) to simulate an observational gage-calibrated WRF-Hydro model to assess shifts in streamflow and flooding potential in three Interior Alaska rivers over a historical period (2008–2017) and two future periods (2038–2047 and 2068–2077). Outputs were assessed for seasonality, streamflow, extreme events, and the comparison between existing flood control infrastructure in the region. The results indicate that streamflow in this region is likely to experience increases in seasonal length and baseflow, while the potential for extreme events and variable short-term streamflow behavior is likely to see greater uncertainty, based on the divergence between the models.

Funder

Strategic Environmental Research and Development Program

NOAA project

NSF grants

Research Computing Systems (RCS) Group at the University of Alaska Fairbanks Geophysical Institute

Publisher

MDPI AG

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