This paper reports the development of a ∼30 m resolution two‐dimensional hydrodynamic model of the conterminous U.S. using only publicly available data. The model employs a highly efficient numerical solution of the local inertial form of the shallow water equations which simulates fluvial flooding in catchments down to 50 km2 and pluvial flooding in all catchments. Importantly, we use the U.S. Geological Survey (USGS) National Elevation Dataset to determine topography; the U.S. Army Corps of Engineers National Levee Database to explicitly represent known flood defenses; and global regionalized flood frequency analysis to characterize return period flows and rainfalls. We validate these simulations against the complete catalogue of Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) maps and detailed local hydraulic models developed by the USGS. Where the FEMA SFHAs are based on high‐quality local models, the continental‐scale model attains a hit rate of 86%. This correspondence improves in temperate areas and for basins above 400 km2. Against the higher quality USGS data, the average hit rate reaches 92% for the 1 in 100 year flood, and 90% for all flood return periods. Given typical hydraulic modeling uncertainties in the FEMA maps and USGS model outputs (e.g., errors in estimating return period flows), it is probable that the continental‐scale model can replicate both to within error. The results show that continental‐scale models may now offer sufficient rigor to inform some decision‐making needs with dramatically lower cost and greater coverage than approaches based on a patchwork of local studies.

[1] We model the spatial distribution of snow depth across a wind-dominated alpine basin using a geostatistical approach with a complex variable mean. Snow depth surveys were conducted at maximum accumulation from 1997 through 2003 in the 2.3 km 2 Green Lakes Valley watershed in Colorado. We model snow depth as a random function that can be decomposed into a deterministic trend and a stochastic residual. Three snow depth trends were considered, differing in how they model the effect of terrain parameters on snow depth. The terrain parameters considered were elevation, slope, potential radiation, an index of wind sheltering, and an index of wind drifting. When nonlinear interactions between the terrain parameters were included and a multiyear data set was analyzed, all five terrain parameters were found to be statistically significant in predicting snow depth, yet only potential radiation and the index of wind sheltering were found to be statistically significant for all individual years. Of the five terrain parameters considered, the index of wind sheltering was found to have the greatest effect on predicted snow depth. The methodology presented in this paper allows for the characterization of the spatial correlation of model residuals for a variable mean model, incorporates the spatial correlation into the optimization of the deterministic trend, and produces smooth estimate maps that may extrapolate above and below measured values.

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The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.