The Arctic Rivers Project

Indigenous communities in Alaska use river systems for subsistence fishing and travel. As climate change rapidly transforms Arctic rivers, the future for these Indigenous people, their fisheries and winter travel corridors are deeply uncertain. This research advances our collective understanding of terrestrial hydrologic change and potential impacts on rivers, fish, and communities in the Arctic. The dissertation facilitates actionable, community-based river discharge, temperature, and ice modeling.

Arctic hydrology is experiencing rapid changes including earlier snow melt, permafrost degradation, increasing active layer depth, and reduced river ice, all of which are expected to lead to changes in stream flow regimes. Recently, long-term (>60 years) climate reanalysis and river discharge observation data have become available. We utilize these data to assess long-term changes in discharge and their hydroclimatic drivers. River discharge during the cold season (October - April) increased by 10% per decade. The most widespread discharge increase occurred in April and October. Compared to the historical period, mean April and October air temperature in the recent period have greater correlation with monthly discharge, indicating that the recent increases in discharge are directly related to air temperature changes.

Expanding the spatial scale, we conduct high-resolution simulations of river discharge and temperature in Alaska and the Yukon River Basin, covering historic and mid-century periods. The simulations involve a chain of river models, including river routing (mizuRoute) and optimized river temperature (River Basin Model) models, forced by a high-resolution (4 km) regional climate model (Regional Arctic System Model) with an optimized land surface model (Community Terrestrial System Model). The river temperature model is optimized using a surrogate-based model optimization method, improving model performance in both seen and unseen river gages. To quantify the impacts of climate change on Alaskan rivers, we employ the pseudo global warming (PGW) method, considering median and high hydroclimate change scenarios derived from the ensemble mean of CMIP6 GCMs under the SSP2-4.5 emissions pathway. The river models indicate mixed discharge changes, with consistently higher river temperatures at mid-century. The projected increases in river temperature and altered discharge will significantly impact Alaskan river ecosystems, with implications for local and Indigenous communities.

To explore change in winter river conditions, we developed novel statistical, machine learning, and remote sensing techniques to quantify river ice conditions. The analysis reveals that ice presence can be accurately discerned from Sentinel-1 images and climate data processed through machine learning models, achieving high accuracies across Alaska. Predicting ice break-up using these methods also yielded high accuracy. Analysis of ice thickness estimation methods demonstrated comparable performance, with root mean square error ranging from 18-23 cm for years or locations not contained in the training data. However, an ensemble approach significantly reduced the RMSE to 13 cm by combining these methods. Ultimately, employing the ensemble model for ice thickness and the machine learning model for ice phenology, we determined ice phenology and thickness for every major Alaskan river. These methods show promise for widespread application in diverse regions, facilitating environmental monitoring and actionable science for local communities.