No abstract

Though urban agriculture (UA), defined here as growing of crops in cities, is increasing in popularity and importance globally, little is known about the aggregate benefits of such natural capital in built‐up areas. Here, we introduce a quantitative framework to assess global aggregate ecosystem services from existing vegetation in cities and an intensive UA adoption scenario based on data‐driven estimates of urban morphology and vacant land. We analyzed global population, urban, meteorological, terrain, and Food and Agriculture Organization (FAO) datasets in Google Earth Engine to derive global scale estimates, aggregated by country, of services provided by UA. We estimate the value of four ecosystem services provided by existing vegetation in urban areas to be on the order of $33 billion annually. We project potential annual food production of 100–180 million tonnes, energy savings ranging from 14 to 15 billion kilowatt hours, nitrogen sequestration between 100,000 and 170,000 tonnes, and avoided storm water runoff between 45 and 57 billion cubic meters annually. In addition, we estimate that food production, nitrogen fixation, energy savings, pollination, climate regulation, soil formation and biological control of pests could be worth as much as $80–160 billion annually in a scenario of intense UA implementation. Our results demonstrate significant country‐to‐country variability in UA‐derived ecosystem services and reduction of food insecurity. These estimates represent the first effort to consistently quantify these incentives globally, and highlight the relative spatial importance of built environments to act as change agents that alleviate mounting concerns associated with global environmental change and unsustainable development.

A nonparametric test that has been used to conclude that extinction rates are periodic with a period of 26 million years is shown to be substantially biased toward this conclusion, regardless of whether or not the data are periodic in origin (and, indeed, regardless of the actual period if they are in fact periodic). The test is shown to be sensitive to measurement error of a type expected with these data (early recording of extinctions due to missing fossil specimens, the "Signor-Lipps effect"), and it is shown that because of the unequal spacing in time, such models may be expected to produce statistically significant but artifactual periods of (in this case) exactly 26 million years over the span of time actually used.

No abstract

Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switchgrass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. The hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States are quantified using the Weather Research and Forecasting Model dynamically coupled to a land surface model (LSM). A suite of continuous (2000-09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted using seasonally evolving biophysical representation of perennial bioenergy cropping systems within the LSM based on observational data. Deployment is carried out only over suitable abandoned and degraded farmlands to avoid competition with existing food cropping systems. Results show that near-surface cooling (locally, up to 5°C) is greatest during the growing season over portions of the central United States. For some regions, principal impacts are restricted to a reduction in near-surface temperature (e.g., eastern portions of the United States), whereas for other regions deployment leads to soil moisture reduction in excess of 0.15-0.2 m 3 m −3 during the simulated 10-yr period (e.g., western Great Plains). This reduction (~25%-30% of available soil moisture) manifests as a progressively decreasing trend over time. The large-scale focus of this research demonstrates the long-term hydroclimatic sustainability of large-scale deployment of perennial bioenergy crops across the continental United States, revealing potential hot spots of suitable deployment and regions to avoid. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. ABSTRACT Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switchgrass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. The hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States are quantified using the Weather Research and Forecasting Model dynamically coupled to a land surface model (LSM). A suite of continuous (2000-09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted using seasonally evolving biophysical representation of perennial bioenergy cropping syst...

No abstract

Tornado damage estimation is important for providing insights into tornado studies and assisting rapid disaster response. However, it is challenging to precisely estimate tornado damage because of the large volumes of perishable data. This study presents data-driven approaches to tornado damage estimation using imagery collected from Unpiloted Aerial Systems (UASs) following the 26 June 2018 Eureka Kansas tornado. High-resolution orthomosaics were generated from Structure from Motion (SfM). We applied deep neural networks (DNNs) on the orthomosaics to estimate tornado damage and assessed their performance in four scenarios: (1) object detection with binary categories, (2) object detection with multiple categories, (3) image classification with binary categories, and (4) image classification with multiple categories. Additionally, two types of tornado damage heatmaps were generated. By directly stitching the resulting image tiles from the DNN inference, we produced the first type of tornado damage heatmaps where damage estimates are accurately georeferenced. We also presented a Gaussian process (GP) regression model to build the second type of tornado damage heatmap (a spatially continuous tornado damage heatmap) by merging the first type of object detection and image classification heatmaps. The GP regression results were assessed with ground-truth annotations and National Weather Service (NWS) ground surveys. This detailed information can help NWS Weather Forecast Offices and emergency managers with their damage assessments and better inform disaster response and recovery.

No abstract