from Data Connects Us: focus on Environment

WINDS OF CHANGE

ALGORITHMS REVEAL “HIDDEN” WEATHER

University of Arizona researchers have devised a way to measure wind using machine learning and algorithms – an innovation that could help predict extreme weather events and save lives in some of Earth’s most populous areas.

The novel approach pulls water vapor information from National Oceanic and Atmospheric Administration (NOAA) satellites. Scientists have long used NOAA raw data for modeling but now can process it with algorithms not available a decade ago.

This approach, led by Xubin Zeng, professor in the College of Science, overcomes limitations that have compromised models for predicting hurricanes, tracking airborne pollutants and more.

Deducing wind data from cloud movements, for example, offers very limited information for certain layers of atmosphere. Similarly, vast areas of the planet, including oceans, have few or no surface stations for pulling measurements from balloon borne sensors.

Wind is a major player on the atmospheric stage, moving not just water vapor but also aerosols like dust and sea salt. It also drives the formation of clouds and precipitation, including extreme weather.

The innovation provides critical data for improving a range of climate and weather models and has given scientists the first comprehensive picture of winds across the tropics and midlatitudes – areas that are home to roughly 90% of Earth’s human population.

The team is now developing a satellite concept optimized for wind research based on this breakthrough and preceding research from the past five years.

WATER, WATER EVERYWHERE

POWERFUL PREDICTION FOR CHANGING WATERWAYS

In response to climate change and increasingly frequent natural disasters, University of Arizona scientists are creating powerful tools that use AI to predict changes across the nation’s web of water systems.

Coastal storm surge can flood freshwater systems with saltwater. Heavy rains overwhelm waterways. Heat and drought dry up streams, concentrate pollutants in evaporating reservoirs and make environments dangerously vulnerable to fire.

Weather can impact water systems in many ways – often with far reaching effects – but sophisticated models are used mainly by researchers because they require extensive expertise. Tools that civil and federal professionals typically use for decision making rely on assumptions and over simplifications from past events.

Laura Condon, assistant professor in the College of Science, is bridging that gap between widespread need and deep expertise. Funded by $5 million from the National Science Foundation, HydroGEN will offer the most comprehensive yet accessible predictive model of the nation’s watersheds.

Named for its ability to generate hydrologic scenarios, HydroGEN uses machine learning and AI to forecast variables like stream flow, soil moisture and groundwater. Computation and data storage for the platform are powered by CyVerse, a 15 year, $117 million investment by the NSF housed in UArizona’s Data Science Institute.

Available publicly online once completed, the system will give community leaders, resource managers and policymakers easy to use, scientifically rigorous tools for decisions on water use for agriculture, managing wildfires and other critical needs.

THIRST FOR KNOWLEDGE

EXPERIMENTS FOR A HOTTER, DRIER WORLD

In an unprecedented experiment, University of Arizona scientists subjected the 30-acre, enclosed rainforest at Biosphere 2 to a three-month drought before reintroducing healthy moisture levels. The resulting data is revealing how plants and soil based microbes responded – knowledge that could prove critical for adapting to a hotter, drier world.

The newest branch of research, led by associate professor Malak Tfaily in the College of Agriculture, Life and Environmental Sciences, reveals what happens underground during and following drought. Throughout the experiment, sensors documented changes in the forest’s rhizosphere – the zone surrounding roots, where complex interactions with soil microbes occur.

Data showed notable drops in the levels of organic compounds that plants pumped out from their roots. Microbes in soil depend on these root exudations for energy. Reducing them can change the composition and activity of microbial communities, altering how they cycle nutrients.

Research also showed that the effects of drought on soil microbes varied with the plant species associated with those microbial communities. The finding suggests that depending on the composition of the ecosystem, drought tolerance strategies can in corporate different interventions to produce different targeted effects below ground.

Finally, soil microbes in drought conditions released more volatile organic compounds (VOCs) and released them later in the day. Researchers theorize this microbial response might increase the potential for cloud cover in real world conditions. Ambient air is hotter later in the day and therefore more likely to carry VOCs high enough to seed cloud formation.

FUTURE FORESTS

A SHRINKING ROLE FOR FORESTS IN CLIMATE CONTROL

Researchers at the University of Arizona Laboratory of Tree Ring Research found that rising temperatures are stunting tree growth, a phenomenon with troubling implications for global warming and climate resilience planning.

Forests remove carbon dioxide (CO2) – the major driver of global warming – from the air, helping to reduce its accumulation in the atmosphere. The study used a first ever fusion of data from tree rings and the U.S. Forest Service census: surveys every 10 years of a forest’s soil quality, its number of trees and tree diameters. The resulting model forecasts that Arizona’s ponderosa pine forests will see tree growth declines of 56% to 91%.

Higher temperatures stress trees’ water transport systems, especially in taller trees, decreasing growth and increasing vulnerability to drought. The study also found that trees in overly dense stands have a stronger negative response to heat. Thinning in those areas and revising fire suppression policies could help alleviate overall forest stress.

Because smaller trees have less capacity for CO2 uptake, the study signals yet another setback in the fight against global warming. Modeling suggests that forests in warming areas around the world can also expect growth declines.

BRIDGING DISCIPLINES

RESEARCH AT THE NEXUS OF GENOMIC & ECOSYSTEM SCIENCES

The University of Arizona BRIDGES* program examines how genes drive impacts that span from individual organisms to entire ecosystems.

Funded by the National Science Foundation, BRIDGES engages outstanding graduate students in projects that trace cascading effects of DNA from biology to ecology to better understand how wild and agricultural systems function and respond to change.

For Changpeng Fan and Sabrina Wilson, the program supports research that explores role of plants and soil microbes in the carbon cycle, both key players in strategies for curbing global warming.

Changpeng Fan, a PhD student in hydrometeorology, is using deep learning technologies to create algorithms that can predict microorganism populations in soil and how much organic matter those microbes decompose.

Because soil plays a key role in the way that carbon cycles through Earth systems, properly accounting for its impact improves our ability to predict levels of carbon dioxide in the atmosphere, which is key to modeling climate change.

Historically, studying these microbe communities has relied on physically collecting soil samples and manually analyzing all of the genetic material they hold. It’s a labor intensive under taking constrained by time, money and geographic accessibility.

As an alternative to that process, Fan is training algorithms on real-world soil samples. As this machine learning advances, AI will be able to increasingly predict the functional composition of soil microbe communities and their impact on carbon sequestration based purely on inputs such as geolocation, local weather and other factors.

Sabrina Wilson, a master’s student in atmospheric science and ecosystem genomics, is working to bring new data inputs to the Community Land Model (CLM 5.0), a tool developed by the National Center for Atmospheric Re search to model Earth systems.

Her research explores how biochar, a soil amendment used in agriculture, could mitigate climate change through its positive effects on soil health and greenhouse gas emissions.

Biochar is a win-win-win in environmental science. Created by applying intense heat and pressure to forest trimmings or plant waste left over from harvests, it helps soil hold nutrients, retain water and nurture microbes, increasing crop yields.

It also reduces carbon dioxide in the atmosphere by making plants better at storing carbon and by using up waste that would otherwise decompose or be burned, releasing greenhouse gasses.

Wilson collected data on a range of biochar applications and their effects on greenhouse gasses and soil. Using AI and machine learning tools at Sandia National Laboratory in New Mexico, she’s now optimizing the relevant parameters in CLM 5.0 and validating the predictive accuracy of those adjustments across environments around the world.

* Building Resources for InterDisciplinary training in Genomic and Ecosystem Sciences

CLIMATE MOMS

WORKING FOR THE FUTURE OUR KIDS DESERVE

Joellen Russell and Beth Tellman at SxSW. Joe C. Klug

University of Arizona climate scientists Joellen Russell and Beth Tellman are self-described “climate moms” – mothers gathering and using climate data to inform decision making today and preserve a better world for their kids and all future generations.

As mothers who also happen to be climate scientists, they study the Earth and human impact on its ecosystems. Dedicated to preserving the planet for the future, they know that to solve the problem of climate change, science must be de mystified and everyday people around the world must demand solutions that preserve the planet for all our kids.

“Why do we do it?” asks Russell. “We’re moms - this is what we do. We want our little ones to have the safe, secure, prosperous future that they deserve and that we grew up with.”

Research by Tellman, assistant professor in the College of Social and Behavioral Sciences, is saving lives and helping to protect economies by revealing how global warming is dramatically increasing flooding. Using satellites, she captures data from flood events around the world and uses machine learning to not only reconstruct past incidents, but also develop algorithms that predict flooding and its destruction with increasing accuracy.

Since flooding sets back progress in many developing countries, the research has far reaching social and economic value. She recently produced a 20 year history of flooding in Bangladesh and trained local stakeholders to use her algorithms to create their own flood maps.

Better flood data can also help with kitchen table economics, potentially reigning in insurance costs and helping families get financial support. A new collaboration using Tellman’s flood maps is helping households prove flood damage to secure FEMA disaster aid.

Research by Russell, a professor in the College of Science, also spans the world, expanding our understanding of the relationship between global warming and our oceans, which both store and release the greenhouse gas carbon dioxide (CO2).

Since 2014, Russell and her team have been building the world’s largest dataset on conditions of the Southern Ocean, using sensor laden robots that float more than half a mile below the frigid water’s surface.

Her studies show that increasingly intense westerly winds – belts of winds that are key to large scale weather patterns in both the northern and southern hemispheres – are driving a massive outgassing of carbon dioxide from deep ocean waters that have held it for nearly a millennium.

That additional CO2 in the atmosphere increases atmospheric warming, which further intensifies wind in a vicious cycle with dangerous effects on global weather systems.

OPEN GOVERNMENT

UNLOCKING 50+ YEARS OF ENVIRONMENTAL DATA

The National Environmental Policy Act (NEPA) requires impact assessments before work proceeds on federally funded projects like highway construction, oil exploration and more. The results of the assessments inform decision makers of possible harm to environmental considerations like animal populations and watersheds.

Since NEPA’s implementation in 1970, the findings from those studies have been stored in hard to access archives, placing a half century of knowledge – thousands of information rich documents – beyond the reach of the public and modern data science.

Now the NEPAccess project, led by the University of Arizona, is unlocking data embedded in environmental impact statements (EIS) and environmental analyses required by NEPA. The goal is to create a more efficient, transparent and accountable tool for science-based, democratic environmental governance.

Using machine learning, the project is turning unstructured documents into structured, searchable data in a model that can also be adapted for transforming other troves of government documents into information ripe for meta analyses.

Devised by a team of data scientists, environ mental researchers and scholars in public policy and law, the process begins with manually pulling content to train machine learning algorithms, enabling automated natural language processing to take over the work of knowledge extraction.

The searchable NEPAccess repository launched in late 2021, and today provides free, full text and filtered searching of EISs with continued work to provide new functionality in response to stake holder input and user experience data.

For the first time, researchers around the world can apply AI, machine learning, georeferencing and other computing tools to study NEPA information across agencies, action types, regions and sectors.