Field-scale crop water consumption estimates reveal potential water savings in California agriculture.

Efficiently managing agricultural irrigation is vital for food security today and into the future under climate change. Yet, evaluating agriculture's hydrological impacts and strategies to reduce them remains challenging due to a lack of field-scale data on crop water consumption. Here, we develop a method to fill this gap using remote sensing and machine learning, and leverage it to assess water saving strategies in California's Central Valley.

Estimating a social cost of carbon for global energy consumption.

Estimates of global economic damage caused by carbon dioxide (CO) emissions can inform climate policy. The social cost of carbon (SCC) quantifies these damages by characterizing how additional CO emissions today impact future economic outcomes through altering the climate. Previous estimates have suggested that large, warming-driven increases in energy expenditures could dominate the SCC, but they rely on models that are spatially coarse and not tightly linked to data.

A generalizable and accessible approach to machine learning with global satellite imagery.

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of satellite imagery can generalize across diverse prediction tasks (e.g.

Global evidence for ultraviolet radiation decreasing COVID-19 growth rates.

With nearly every country combating the 2019 novel coronavirus (COVID-19), there is a need to understand how local environmental conditions may modify transmission. To date, quantifying seasonality of the disease has been limited by scarce data and the difficulty of isolating climatological variables from other drivers of transmission in observational studies. We combine a spatially resolved dataset of confirmed COVID-19 cases, composed of 3,235 regions across 173 countries, with local environmental conditions and a statistical approach developed to quantify causal effects of environmental conditions in observational data settings.