We simulated tsunami propagation for several scenario slip distributions for the 1938 MW 8.3 earthquake along the Alaska Peninsula, and compared these to the observed records at Unalaska/Dutch Harbor and Sitka. The Sitka record is sensitive to the depth of slip but not the along-strike location, and is fit best by slip at shallow depth. The Unalaska record is sensitive mainly to the along-strike location of slip, and is fit best by slip that is concentrated in the eastern part of the presumed 1938 rupture zone. The tsunami data show that the actual 1938 earthquake rupture zone was smaller than previously thought, likely ~200 km in length, and had no slip near the Shumagin Islands or in the 2020 Simeonof earthquake’s rupture zone. The rupture models that best predict the 1938 tsunami lie within the region of high present day slip deficit inferred from GPS.

A slope at Barry Arm, in Alaska’s Prince William Sound, is deforming at a varying rate up to tens of meters per year above a retreating glacier and deep fjord that is a popular recreational destination. If the estimated 500 million cubic meters of unstable material on this slope were to fail catastrophically, the impact of the landslide with the ocean would produce a tsunami that would not only endanger those in its immediate vicinity, but likely also those in more distant areas such as the port of Whittier, 50 km away. The discovery of this threat was happenstance, and the response so far has been cobbled together from over a dozen existing grants and programs. Remotely sensed imagery could have revealed this hazard a decade ago, but nobody was looking, highlighting our lack of coordination and preparedness for this growing hazard driven by climate change. As glaciers retreat, they can simultaneously destabilize mountain slopes and expose deep waters below, creating the potential for destructive tsunamis. The settings where this risk might occur are easily identified, but more difficult to assess and monitor. Unlike for volcanoes, active faults, landslides, and tectonic tsunamis, the US has conducted no systematic assessment of tsunamis generated by subaerial landslides, nor has the US established methods for monitoring or issuing warnings for such tsunamis. The U.S. National Tsunami Warning Center relies on seismic signals and sea-level measurements to issue warnings; however, landslides are more difficult to detect than earthquakes, and the resultant tsunamis often would reach vulnerable populations and infrastructure before water level gages could help estimate the magnitude of the tsunami. Also, integrating precursory motion and other clues of an impending slope failure into a tsunami warning system has only been done outside the US (e.g Norway: Blikra et al., 2012). Barry Arm is a dramatic case study highlighting these challenges and may provide a model for mitigating the threat of tsunamis generated by subaerial landslides enabled by glacial retreat elsewhere.

Soil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data-sparse region. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically-based, one-dimensional ecohydrologic Simultaneous Heat and Water (SHAW) model, loosely coupled with the Geophysical Institute of Permafrost Laboratory (GIPL) model, to an upland deciduous forest stand in interior Alaska over a 13-year period. Using a Generalized Likelihood Uncertainty Estimation (GLUE) parameterization, SHAW reproduced interannual and vertical spatial variability of soil moisture during a five-year validation period quite well, with root mean squared error (RMSE) of volumetric water content at 0.5 m as low as 0.020. Many parameter sets reproduced reasonable soil moisture dynamics, suggesting considerable equifinality. Model performance generally declined in the eight-year validation period, indicating some overfitting and demonstrating the importance of interannual variability in model evaluation. We compared the performance of parameter sets selected based on traditional performance measures (RMSE) that minimize error in soil moisture simulation, with those that were designed to minimize the dependence of model performance on interannual climate variability. The latter case moderately decreases traditional model performance but is likely more suitable for climate change applications, for which it is important that model error is independent from climate variability. These findings illustrate (1) that the SHAW model, coupled with GIPL, can adequately simulate soil moisture dynamics in this boreal deciduous region, (2) the importance of interannual variability in model parameterization, and (3) a novel objective function for parameter selection to improve applicability in non-stationary climates.