Drylands cover 40% of the global terrestrial surface and provide important ecosystem services. While drylands as a whole are expected to increase in extent and aridity in coming decades, temperature and precipitation forecasts vary by latitude and geographic region suggesting different trajectories for tropical, subtropical, and temperate drylands. Uncertainty in the future of tropical and subtropical drylands is well constrained, whereas soil moisture and ecological droughts, which drive vegetation productivity and composition, remain poorly understood in temperate drylands. Here we show that, over the twenty first century, temperate drylands may contract by a third, primarily converting to subtropical drylands, and that deep soil layers could be increasingly dry during the growing season. These changes imply major shifts in vegetation and ecosystem service delivery. Our results illustrate the importance of appropriate drought measures and, as a global study that focuses on temperate drylands, highlight a distinct fate for these highly populated areas.

Erosion by wind is one of the principal processes associated with land degradation in drylands and is a significant concern to land managers and policymakers globally. In the drylands of North America, millions of tons of soil are lost to wind erosion annually. Of the 60 million ha in the United States identified as most vulnerable to wind erosion (arid and dominated by fine sandy soils), 64% are managed by federal agencies (37 million ha). Here we review the drivers and consequences of wind erosion and dust emissions on drylands in the United States, with an emphasis on actionable responses available to policymakers and practitioners. We find that while dryland soils are often relatively stable when intact, disturbances including fire, domestic livestock grazing, and off‐highway vehicles can increase horizontal eolian flux by an order of magnitude, in some cases as much as 40‐fold. A growing body of literature documents the large‐scale impacts of deposited dust changing the albedo of mountain snow cover and in some cases reducing regional water supplies by ~5%. Predicted future increases in aridity and extreme weather events, including drought, will likely increase wind erosion and consequent dust generation. Under a drier and more variable future climate, new and existing soil‐ and vegetation‐disturbing practices may interact in synergistic ways, with dire consequences for environments and society that are unforeseen to many but fairly predictable given current scientific understanding. Conventional restoration and reclamation approaches, which often entail surface disturbance and rely on adequate moisture to prevent erosion, also carry considerable erosion risk especially under drought conditions. Innovative approaches to dryland restoration that minimize surface disturbance may accomplish restoration or reclamation goals while limiting wind erosion risk. Finally, multidisciplinary and multijurisdictional approaches and perspectives are necessary to understand the complex processes driving dust emissions and provide timely, context‐specific information for mitigating the drivers and impacts of wind erosion and dust.

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.