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Recent hurricane seasons left many communities wondering if this is this the new normal. Digital infrastructure designed for citizen data collection may help these communities increase resilience. 

Abstract. Numerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon–Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components – modular implementations of single physical processes – and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components – for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab. 

Abstract Modeled stream discharge is often used to drive sediment transport models across channel networks. Because sediment transport varies non‐linearly with flow rates, discharge modeled from daily total precipitation distributed evenly over 24‐hr may significantly underestimate actual bedload transport capacity. In this study, we assume bedload transport capacity determined from a hydrograph resulting from the use of hourly (1‐hr) precipitation is a close approximation of actual transport capacity and quantify the error introduced into a network‐scale bedload transport model driven by daily precipitation at channel network locations varying from lowland pool‐riffle channels to upland colluvial channels in a watershed where snow accumulation and melt can affect runoff processes. Transport capacity is determined using effective stresses and the Wilcock and Crowe (2003) equations and expressed in terms of transport capacity normalized by the bankfull value. We find that, depending on channel network location, cumulative error can range from 10% to more than two orders of magnitude. Surprisingly, variation in flow rates due to differences in hillslope and channel runoff do not seem to dictate the network locations where the largest errors in predicted bedload transport capacity occur. Rather, spatial variability of the magnitude of the effective‐bankfull‐excess shear stress and changes in runoff due to snow accumulation and melt exert the greatest influence. These findings have implications for flood‐hazard and aquatic habitat models that rely on modeled sediment transport driven by coarse‐temporal‐resolution climate data. 

Abstract A mountain watershed network model is presented for use in decadal to centurial estimation of source‐to‐sink sediment dynamics. The model requires limited input parameters and can be effectively applied over spatial scales relevant to management of reservoirs, lakes, streams, and watersheds (1–100 km²). The model operates over a connected stream network of Strahler‐ordered segments. The model is driven by streamflow from a physically based hydrology model and hillslope sediment supply from a stochastic mass wasting algorithm. For each daily time step, segment‐scale sediment mass balance is computed using bedload and suspended load transport equations. Sediment transport is partitioned between grain size fractions for bedload as gravel and sand, and for suspended load as sand and mud. Bedload and suspended load can deposit and re‐entrain at each segment. We demonstrated the model in the Elwha River Basin, upstream of the former Glines Canyon dam, over the dam's historic 84‐year lifespan. The model predicted the lifetime reservoir sedimentation volume within the uncertainty range of the measured volume (13.7–18.5 million m³) for 25 of 28 model instances. Gravel, sand, and mud fraction volumes were predicted within measurement uncertainty ranges for 18 model instances. The network model improved the prediction of sediment yields compared to at‐a‐station sediment transport capacity relations. The network model also provided spatially and temporally distributed information that allowed for inquiry and understanding of the physical system beyond the sediment yields at the outlet. This work advances cross‐disciplinary and application‐oriented watershed sediment yield modeling approaches.