This paper reports a new and significantly enhanced analysis of US flood hazard at 30m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level and inclusion of the impact of local interventions in the flooding system. For the first time we consider pluvial, fluvial and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centred on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high quality local models and the entire catalogue of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69-0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near future time horizon we project clear patterns of changing flood hazard (-3.8 to +16% changes in 100yr inundated area at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defence protection may become compromised by this time.

Satellite imagery to rapidly develop maps of historical flood hazard and current inundated areas over large spatial coverage is indispensable in supporting situational awareness for improved debris estimation, transportation impacts and damage assessments. However, how best to utilize these maps as actionable information during flood disasters and for flood disaster response assistance is less clear. Furthermore, the integration of any satellite data from an “untrusted” (non-mandated) source into the operations chain and response protocols of a mandated agencies such as FEMA, PDC (PDC is already pulling some DFO-DSS layers) or the UN WFP would be a non-trivial procedure. These agencies desire to prioritize support and resource requirements for community lifelines. (Safety & Security; Food, Water & Shelter; Health & Medical; Energy (Power & Fuel); Communications; Transportation; and Hazardous Materials). The majority of these lifelines can be impacted by floods. The Global Flood Observatory’s (DFO, University of Colorado Boulder) web map server and its associated mobile app (DFO-Floods) is a resource for global extents of floods now delivered as map products via web services. This flood decision support system (DSS) serves flood maps along with other trusted geospatial data to the global disaster response community. However, acceptance of the DFO product line as a trusted information source requires additional tests to assess its performance in combination with the respective response process of agencies around the world. This would allow moving the product from a high Application Readiness Level into an Operational Readiness Level (ORL) for agency trusted data implementation. This paper reviews success examples of the DFO flood layers, illustrates the newly released mobile app and discusses the need for trusted flood map products and services to support the global disaster response community.

Flooding is a frequent disaster that has a wide-spread footprint globally with significant financial and societal impacts. With availability of Earth observation data from private and public entities at varying spatial, temporal, and spectral resolution as well as data from crowdsourcing, there is no shortage of models. In fact, models and algorithms are abundant and proliferating. However, the question remains where is a global flood model when we need one? Just because models are available does not mean they are usable or accessible and adequate for emergency managers, first responders and other stakeholders who use the model outputs for preparedness, response and resource planning. Often the issue of usability stems from the fact that the models are not always reproducible or replicable. The accuracy and uncertainty associated with the models and how they change based on the scale of analysis and the resolution of input and output datasets are often not communicated properly to stakeholders so they can be part of their decision-making process. The proliferation of machine learning and data driven models that rely on historical data also adds to this problem. This paper discusses several important issues associated with global flood models and provides recommendations that could be used to increase the usability of these models.