With increasing climate variability, natural disaster frequency, and populations there is an emerging need for digital awareness of the built environment. Knowing who and what is where is critical to better allocating resources in emergencies, planning for the future, and translating the outcomes of different environmental models into tangible impacts that help bridge the policy-science gap. To date, a range of open source projects have sought to represent the built environment, but each has focused on either spatial completeness (e.g. AI methods), accuracy (hand digitization in OpenStreetMap), or attribute richness (OpenAddresses). The reality is that all of these are needed for different uses, and integrating these datasets offer a unique path towards a spatial and attribute rich data product. This work describes a method for developing a best available Built Environment Asset Registry (BEAR) from a suite of open - yet disparate - data sources. To date, BEAR conflates commonly utilized, continental-scale footprint and address databases covering the contiguous US. The resulting product is developed as a cloud-native database that conforms to an extensible schema. Using this core representation, we highlight efficient access patterns for retrieving subsets of BEAR from the Lynker Spatial data portal, and examples of applying BEAR to hazard modeling and social vulnerability assessment. We show how this product can increase the skill and utility of forecasts related to flooding and fires.

Rating curves are commonly developed through direct observation, open channel flow models, or mechanical methods, each relying on in-situ measurement. As part of a U.S. effort to provide high resolution, continental scale, flood mapping, synthetic rating curves (SRCs) were developed across the National Hydrography Dataset (NHDPlusV2) to translate flows, like those generated by the NOAA National Water Model, into river depths. This approach uses Digital Elevation Models (DEM) to define the necessary cross-sectional properties for Manning’s equation. A significant limitation, alongside an opportunity for broad improvement, has been assigning suitable roughness without local information. We applied the DEM based methodology to generate SRCs at 7,270 locations with known USGS rating curves, and calibrated roughness to minimize the error between predicted and observed flow. Subsequently, we tested several approaches based on land cover, stream order, and the hydrographic network to estimate the optimized values in a manner that can be extended to ungauged catchments. Among these, a predictive Machine Learning (ML) model based on the NHDPlusV2 network attributes demonstrated superior ability to estimate the optimized roughness with a Spearman correlation of 0.89. Sensitivity analysis showed improving accuracy of DEM and roughness is crucial for accurate estimation of the lower and mid/upper parts of SRC, respectively. Finally, we applied the predictive model over the NHDPlusV2, generating reach-level roughness estimates that can directly support national flood mapping efforts. The method is generalizable to any hydrofabric network that contains topology, however the generated values are dependent on the DEM and hydrofabric used.