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Abstract. pyTopoComplexity is a Python package designed for efficient and customizable quantification of topographic complexity using four advanced methods: two-dimensional continuous wavelet transform analysis, fractal dimension estimation, rugosity index, and terrain position index calculations. This package addresses the lack of open-source software for these advanced terrain analysis techniques essential for modern geomorphology and geohazard research, enhancing data comparison and reproducibility. By assessing topographic complexity across multiple spatial scales, pyTopoComplexity allows users to identify characteristic morphological scales of studied landforms. The software repository also includes a Jupyter Notebook that integrates components from the surface-process modeling platform Landlab (Hobley et al., 2017), facilitating the exploration of how terrestrial processes, such as hillslope diffusion and stream power incision, drive the evolution of topographic complexity over time. When these complexity metrics are calibrated with absolute age dating, they offer a means to estimate in situ hillslope diffusivity and fluvial erodibility, which are critical factors in determining the efficiency of landscape recovery after significant geomorphic disturbances such as landslides. By integrating these features, pyTopoComplexity expands the analytical toolkit for measuring and simulating the time-dependent persistence of geomorphic signatures against environmental and geological forces. 

ABSTRACT Earthquake-induced landslides can record information about the seismic shaking that generated them. In this study, we present new mapping, Light Detection and Ranging-derived roughness dating, and analysis of over 1000 deep-seated landslides from the Puget Lowlands of Washington, U.S.A., to probe the landscape for past Seattle fault earthquake information. With this new landslide inventory, we observe spatial and temporal evidence of landsliding related to the last major earthquake on the Seattle fault ∼1100 yr before present. We find spatial clusters of landslides that correlate with ground motions from recent 3D kinematic models of Seattle fault earthquakes. We also find temporal patterns in the landslide inventory that suggest earthquake-driven increases in landsliding. We compare the spatial and temporal landslide data with scenario-based ground motion models and find stronger evidence of the last major Seattle fault earthquake from this combined analysis than from spatial or temporal patterns alone. We also compare the landslide inventory with ground motions from different Seattle fault earthquake scenarios to determine the ground motion distributions that are most consistent with the landslide record. We find that earthquake scenarios that best match the clustering of ∼1100-year-old landslides produce the strongest shaking within a band that stretches from west to east across central Seattle as well as along the bluffs bordering the broader Puget Sound. Finally, we identify other landslide clusters (at 4.6–4.2 ka, 4.0–3.8 ka, 2.8–2.6 ka, and 2.2–2.0 ka) in the inventory which let us infer potential ground motions that may correspond to older Seattle fault earthquakes. Our method, which combines hindcasting of the surface response to the last major Seattle fault earthquake, using a roughness-aged landslide inventory with forecasts of modeled ground shaking from 3D seismic scenarios, showcases a powerful new approach to gleaning paleoseismic information from landscapes.