In the current literature, the life cycle, technoeconomic, and resource assessments of microalgae-based biofuel production systems have relied on growth models extrapolated from laboratory-scale data, leading to a large uncertainty in results. This type of simplistic growth modeling overestimates productivity potential and fails to incorporate biological effects, geographical location, or cultivation architecture. This study uses a large-scale, validated, outdoor photobioreactor microalgae growth model based on 21 reactor-and species-specific inputs to model the growth of Nannochloropsis. This model accurately accounts for biological effects such as nutrient uptake, respiration, and temperature and uses hourly historical meteorological data to determine the current global productivity potential. Global maps of the current near-term microalgae lipid and biomass productivity were generated based on the results of annual simulations at 4,388 global locations. Maximum annual average lipid yields between 24 and 27 m , corresponding to biomass yields of 13 to 15 g·m, are possible in Australia, Brazil, Colombia, Egypt, Ethiopia, India, Kenya, and Saudi Arabia. The microalgae lipid productivity results of this study were integrated with geography-specific fuel consumption and land availability data to perform a scalability assessment. Results highlight the promising potential of microalgae-based biofuels compared with traditional terrestrial feedstocks. When water, nutrients, and CO 2 are not limiting, many regions can potentially meet significant fractions of their transportation fuel requirements through microalgae production, without land resource restriction. Discussion focuses on sensitivity of monthly variability in lipid production compared with annual average yields, effects of temperature on productivity, and a comparison of results with previous published modeling assumptions. algae | global model | geographic information system | life cycle assessment | dynamic map R ecent volatility in oil prices, attributed to increased demand and limited resources, has led to the development of unconventional petroleum reserves, such as oil sands, and increased exploration of alternative and renewable fuel sources. Scalability limitations associated with traditional terrestrial biofuel feedstocks have renewed interest in next-generation feedstocks, such as microalgae. Microalgae offer many potential advantages over traditional terrestrial oil crops, including higher lipid productivities, a lack of competition for arable land, year-round cultivation, integration with saline and low-quality water sources, and a viable drop-in equivalent fuel product (1-5). These scalable advantages make microalgae a promising feedstock for biofuel production and a potential sustainable alternative to traditional petroleum fuels.The current near-term productivity potential for microalgae at large-scale currently is being estimated through the linear scaling of laboratory-based growth and lipid data, which has led to a large variance in report...

In highly impaired watersheds, it is critical to identify both areas with desirable habitat as conservation zones and impaired areas with the highest likelihood of improvement as restoration zones. We present how detailed riparian vegetation mapping can be used to prioritize conservation and restoration sites within a riparian and instream habitat restoration program targeting 3 native fish species on the San Rafael River, a desert river in southeastern Utah, United States. We classified vegetation using a combination of object‐based image analysis (OBIA) on high‐resolution (0.5 m), multispectral, satellite imagery with oblique aerial photography and field‐based data collection. The OBIA approach is objective, repeatable, and applicable to large areas. The overall accuracy of the classification was 80% (Cohen's κ = 0.77). We used this high‐resolution vegetation classification alongside existing data on habitat condition and aquatic species' distributions to identify reaches' conservation value and restoration potential to guide management actions. Specifically, cottonwood (Populus fremontii) and tamarisk (Tamarix ramosissima) density layers helped to establish broad restoration and conservation reach classes. The high‐resolution vegetation mapping precisely identified individual cottonwood trees and tamarisk thickets, which were used to determine specific locations for restoration activities such as beaver dam analogue structures in cottonwood restoration areas, or strategic tamarisk removal in high‐density tamarisk sites. The site prioritization method presented here is effective for planning large‐scale river restoration and is transferable to other desert river systems elsewhere in the world.

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