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.

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.

Terrestrial primary production is a fundamental ecological process and a crucial component in understanding the flow of energy through trophic levels. The global MODIS gross primary production (GPP) and net primary production (NPP) products (MOD17) are widely used for monitoring GPP and NPP at coarse resolutions across broad spatial extents. The coarse input datasets and global biome-level parameters, however, are well-known limitations to the applicability of the MOD17 product at finer scales. We addressed these limitations and created two improved products for the conterminous United States (CONUS) that capture the spatiotemporal variability in terrestrial production. The MOD17 algorithm was utilized with medium resolution land cover classifications and improved meteorological data specific to CONUS in order to produce: (a) Landsat derived 16-day GPP and annual NPP at 30 m resolution from 1986 to 2016 (GPP L30 and NPP L30 , respectively); and (b) MODIS derived 8-day GPP and annual NPP at 250 m resolution from 2001 to 2016 (GPP M250 and NPP M250 respectively). Biome-specific input parameters were optimized based on eddy covariance flux tower-derived GPP data from the FLUXNET2015 database. We evaluated GPP L30 and GPP M250 products against the standard MODIS GPP product utilizing a select subset of representative flux tower sites, and found improvement across all land cover classes except croplands. We also found consistent interannual variability and trends across NPP L30 , NPP M250 , and the standard MODIS NPP product. We highlight the application potential of the production products, demonstrating their improved capacity for monitoring terrestrial production at higher levels of spatial detail across broad spatiotemporal scales.

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform ( https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Abstract:Movement of liquid water through snowpacks remains one of the least understood aspects of snow hydrology. Liquid water movement through snowpacks is generally recognized to occur in distinct flow paths rather than as uniform flow through a homogeneous porous medium. Dye tracer experiments have been used in studies of meltwater flow through snow since the 1930s. Although dye tracer experiments have provided valuable qualitative information about meltwater pathways, quantitative descriptions of their spacing and location are not commonly available because of the difficulty in precisely excavating and measuring pathways. Here we provide a new proof-of-concept instrument we term a 'snow guillotine' that provides more quantitative information from dye tracer experiments conducted on melting snowpacks. Photographs are taken of each crosssection over a 1-m distance. Application of image processing and geostatistical analysis allows collection of high-resolution (1 cm 3 ), three-dimensional data on meltwater flow through a snowpack. The results show preferential flowpaths, with the majority of vertical flow occurring in the upper 20-55 cm of the snowpack, while fewer preferential flowpaths are apparent below 100 cm. The number of vertical flowpaths in the upper half of the snowpack averaged almost 100 per m 2 , with the highest number of flowpaths reaching almost 300 per m 2 . Layer interfaces were found to significantly increase the volume of dye, indicating dominance by lateral flow at these boundaries. At each stratigraphic interface, the number of individual clusters decreased and it was more likely for a dyed pixel to be part of a large cluster. Geostatistical analyses showed that there were large increases in correlation lengths and the connectivity function at stratigraphic layers in contrast to low values between layers. For example, the buried ice layer in Experiment A at 169-170 cm showed separation distances of 20 cm. In contrast, two rows above this layer the separation distance was only 2 cm. Implementation of the snow guillotine provides the ability to conduct geostatistical analyses on field measurements of meltwater flow while providing three-dimensional, quantitative data of unprecedented spatial resolution.