Oceanography and Atmospheric Sciences and Meteorology Commons^™

Deep Learning-Based Burned Area Mapping Of California Wildfires Using Sentinel-2 And Landsat-8 Imagery Enhanced With Super-Resolution Techniques, Youngmin Seo, Seung Hee Kim, Menas Kafatos, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

The increasing frequency of wildfires under a changing climate has led to extensive ecosystem destruction, highlighting the need for reliable burned area assessment using satellite imagery. Single-satellite data are constrained by observation gaps and interference from smoke and clouds, whereas multi-satellite data fusion can mitigate these limitations. Nonetheless, the fusion techniques still encounter challenges such as spatial information loss from resolution differences and cross-satellite domain mismatch. This study presents a burned area mapping framework that integrates super-resolution (SR) with transfer learning to address spatial and domain gaps in multi-satellite data. Specifically, Landsat-8 imagery is super-resolved to 7.5 m resolution, and …

Understanding And Predicting Precipitation Characteristics In The United States Through Machine Learning, Numerical Modeling And Measurements, Cody Luther Ratterman

All Graduate Theses and Dissertations, Fall 2023 to Present

Precipitation is one of the most important yet uncertain variables in the climate system. It varies dramatically in terms of timing, accumulation, rate, and phase, depending on location, season, circulation patterns, and atmospheric conditions. Precipitation forecasts, especially snowfall, are essential to supporting drought mitigation and water management in the Intermountain West. Because rainfall and snowfall lead to opposite effects on snowpack, accurately partitioning rain and snow is important to estimate snowpack levels, winter recreation, mountain ecosystems and runoff. The research findings in this dissertation have advanced the understanding and prediction of precipitation and snowpack in the U.S. by addressing the …

Clearing The Air: Tracking Spatial And Temporal Pm2.5 Variability Along A Biking Transect In Chicago, Beau R. Rass

DePaul Discoveries

Fine particulate matter (PM₂.₅) poses significant risks to human health and disproportionately affects marginalized communities in urban environments (World Health Organization [WHO], 2021; Tessum et al., 2021). Using low-cost mobile sensors, this study explored spatiotemporal PM₂.₅ concentration patterns along a north–south transect of Halsted Street in Chicago. A 2B Technologies Portable Aerosol Monitor (PAM) mounted on a bicycle was used to continuously record PM₂.₅ concentrations with associated GPS coordinates at approximately two-second intervals during 16 sampling events between 08-12-2025 and 10-05-2025. PM₂.₅ concentrations did not differ significantly among the South Side, West Loop, and North Side regions, contrary to the …

Western Kentucky University Stormwater Utility Survey 2026, Warren Campbell

SEAS Faculty Publications

In the U.S., more than 2200 stormwater utilities have been enacted. This year, we have expanded the survey to include stormwater utilities in other countries. Many of these stormwater assessments are called taxes, but if the assessment is recurring, is not an ad valorem tax, and is dedicated to stormwater purposes, we call it a stormwater utility. With the help of ChatGPT we have been able to identify 25 stormwater fees in Australia. We were also able to identify 3 in New Zealand, and some in Europe as well.

Prediction Of Future Mushroom Fruiting Success And Dispersion Based On Models Of Changing Surface Air Temperature In Oaxaca, Mexico, Frida Martinez

ATU Scholars Symposium

Mushrooms serve various purposes, including medicinal, culinary, and cultural significance for indigenous groups, as well as recreational and ecosystem services. Oaxaca, Mexico, is a hotspot for mushroom biodiversity including the following varieties: Amanita muscaria, Schizophyllum commune and Coprinellus disseminates. The purpose of this study is to determine if a changing Mean Surface Air Temperature will have an influence on these mushrooms. We obtained, analyzed, and compared historical and future climate data from the World Bank Group Climate Change Knowledge Portal to identify if any of the mushroom species would be impacted. The study revealed a general trend of warming Mean …

Changes In Land, Ocean, Atmospheric Parameters Associated With The 2025 Myanmar (Mw 7.7) Earthquake, Feng Jing, Akshansa Chauhan, Ashwani Raju, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Multiple parameters associated with the land, atmosphere, and ocean were analyzed to study short-term and immediate pre-earthquake changes associated with the 28 March 2025 Myanmar earthquake (Mw 7.7). Anomalous clear-sky outgoing longwave radiation (ClrOLR) and trace gases (CH₄, CO, and O₃) were detected within two months prior to the mainshock. Vertical changes at different pressure levels suggest a possible underground source. High-temporal-resolution observations of the infrared brightness temperature and surface air pressure revealed short-lived fluctuations shortly before the earthquake, which may reflect localized stress adjustments and surface latent heat flux release during the final stage of earthquake preparation. In the …

Quantifying Global Internal Displacement Risk At The Hazard-Vulnerability-Conflict Nexus, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Hesham Morgan, Ali Elgendy, Susan Mikhail, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

This study introduces a novel, diagnostic hazard-specific displacement risk index that integrates high-resolution hazard data with social vulnerability metrics and historical displacement records, uncovering overlooked compound risks in conflict areas and links between environmental stress and instability. Our methodology combines historical displacement records with environmental, demographic, and socioeconomic indicators to identify high-risk regions and quantify interactions between hazards and conflict across six major hazard types. Results reveal floods as the primary displacement driver, particularly in South Asia (Bangladesh, India, Pakistan) and Sub-Saharan Africa (Nigeria, Ethiopia), where dense populations in flood-prone areas intersect with low socioeconomic resilience. Droughts disproportionately impact arid …

Bioclimatic Reorganization Of Carbon–Water–Energy Coupling Revealed By Eddy Covariance And Interpretable Machine Learning, Koong Yi, Margaret S. Torn, Gabriel B. Senay, Lixin Wang, Kosana Suvočarev, Joshua B. Fisher, Arman Ahmadi, Housen Chu, Gil Bohrer, Georgianne W. Moore, Stephen P. Good, Steven A. Kannenberg, Justine E. C. Missik, Kanishka Mallick, Kul Khand, Youngryel Ryu, Kuno Kasak, A. Christopher Oishi, David E. Reed, Xiangzhong Luo, Carl J. Bernacchi, Dennis Baldocchi

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Ecosystem exchanges of carbon, water, and energy are central to Earth system functioning, yet their sensitivities to environmental variability remain poorly constrained across biomes and climates. Here, we analyzed ≥ 5 years of eddy covariance data from 87 AmeriFlux sites (964 site-years) spanning six vegetation types and a broad range of climatic conditions to examine the controls and multi-year trends of gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency (WUE), and the Bowen ratio. We trained boosted regression tree ensembles with environmental (air temperature, vapor pressure deficit, soil water content, atmospheric CO2, radiation, wind speed) and temporal (month, year) variables …

Constraints To Adopting Locally Based Innovation Systems In Adapting To Climate Change Impacts In Serengeti District, Tanzania, Donald Mwiturubani

Journal of Humanities and Social Sciences

Smallholder farmers in rural Tanzania depend mainly on rain-fed agriculture, which makes them vulnerable to the impacts of climate change. This paper is based on a study conducted in Burunga and Musati villages in Serengeti District, Tanzania. The study employed a mixed methods approach; combining qualitative and quantitative methods. It involved 154 participants and employed household surveys, key informant interviews, and focus group discussions as data collection methods. The findings suggest that smallholder farmers in the study area are aware of climate change and do, indeed, develop, adopt, and use locally-based innovation systems that avert or minimise its impacts. Smallholder …

Peruvian Pinniped Teeth Trace Element Data-Tracking Ecosystem Variabilites Through Trace Element Record Found In Peruvian Pinniped Dentin, Dimitrios Giarikos, Amy Hirons, Ally E. Sheehan, Radleigh Santos

SECLER Data

The South American sea lion (Otaria byronia; SASL) and the Peruvian fur seal (Arctocephalus australis; PFS) are apex predators within the Humboldt Current ecosystem, where population dynamics respond to both natural variability and anthropogenic pressures, including El Niño–Southern Oscillation (ENSO) events and coastal mining. This study quantified 18 trace elements (TEs) in canine dentin from both species collected at Punta San Juan, Peru using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to evaluate long-term environmental exposure. Because dentin is deposited incrementally, it preserves a chronological chemical archive reflecting ontogenetic shifts in diet and habitat use. Overall, TE concentrations were broadly similar …

Long-Term Variability Of Air Quality And Greenhouse Gas Emissions From Rice Crop Burning In Punjab During 2012–2020, Harsimranjit Kaur Romana, Dericks Praise Shukla, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Punjab, India's primary rice and wheat production hub, has witnessed rapid expansion of paddy cultivation over the past two decades, driven by minimum support price incentives, changes in government policies, alignment of sowing with the monsoon season and the adoption of high-yielding varieties. This transition has intensified groundwater extraction and shortened the fallow period between rabi and kharif crop seasons, reducing the window between rice harvesting and wheat sowing, leading to widespread open-field burning of rice residue and recurrent post-monsoon air-quality deterioration across the Indo-Gangetic Plain. Despite numerous short-term or single-pollutant assessments, a spatially resolved, multi-pollutant and multi-decadal evaluation linking …

Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee

Institute for ECHO Articles and Research

Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …

A Probabilistic Deep Learning Framework For Retrieving Chlorophyll-A From Hyperspectral Imagery: Integrating Channel Attention And Mixture Density Networks, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Junde Chen, Hesham Morgan, Michael J. Garay, Olga V. Kalashnikova, Shahryar Fazli, Charles Ichoku, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Accurate monitoring of Chlorophyll-a (Chla) is critical for assessing aquatic ecosystem health, yet ecological complexity often leads to ambiguous spectral signatures in satellite data. Traditional deterministic models assume a one-to-one mapping between spectra and pigments, often failing to capture these high-dimensional analytical challenges. In this study, we propose a novel deep learning architecture, the Channel Attention-Mixture Density Network (CA-MDN), to retrieve Chla from the National Aeronautics and Space Administration (NASA) Earth Surface Mineral Dust Source Investigation (EMIT) hyperspectral mission. The CA-MDN integrates an attention mechanism to dynamically select ecologically relevant spectral bands and employs a probabilistic output layer to quantify …

Satellites, Urban Heat, And Environmental Justice: Community As The Bridge Between Analysis And Action, Joshua B. Fisher, Ambar Rivera, Ava Cison, Ashley Agatep, Kainani Tacazon, Sophia Spiegleman, Alison Mckenery, Rio E. Fisher, Reginald Archer, Jason A. Douglas

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Heat waves are increasing in frequency, intensity, magnitude, and duration, causing a disproportionate impact on marginalized communities exposed to urban heat islands. Newly emerging spaceborne thermal sensing instruments, such as ECOSTRESS and Hydrosat, now have the capabilities to measure urban surface temperatures accurately at the block level (< 100 m) and with enough frequency to capture transient heat waves (daily to subweekly). Such data are critical for monitoring and informing policy and mitigation efforts, such as resurfacing, green space, cooling stations, and medical mobilization. These serve to advance environmental justice and reduce health risks—and deaths—among the most vulnerable: minority, low-income, elderly, those with physical- and mental-health preconditions, unhoused, children, and outdoor workers. While scientists have increasingly used satellite data to quantify urban heat islands and risks to communities, there remains a significant gap in action resulting from such analyses—a figurative and literal “valley of death.” Reviewing over 500 scientific publications, we identify a critical lack of engagement with the communities being analyzed (10.9%; n = 58); yet, community engagement is key to bridging such analysis with subsequent action. Here, we demonstrate how participatory community engagement directly with data and analysis leads to increased policy changes and mitigation efforts. Our framework has immediate implications for how scientists may augment their work and thought processes to achieve …

A Robust Deep Learning Ensemble Framework For Waterbody Detection Using High-Resolution X-Band Sar Under Data-Constrained Conditions, Soyeon Choi, Seung Hee Kim, Son V. Nghiem, Menas Kafatos, Minha Choi, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, …

Chesapeake Bay Water Clarity: Challenges And Successes, Jessica S. Turner, Carl T. Friedrichs, David B. Parrish, Kelsey A. Fall

OES Faculty Publications

The Chesapeake Bay is a large estuarine system spanning multiple jurisdictions and serves as a model for estuarine health worldwide. Historically, nutrient loading degraded water quality, prompting the need for regulation. Water clarity, one component of water quality, is vital for benthic communities and serves as a key indicator of overall ecosystem health. Here, physical resuspension and salinity gradients, nutrient and sediment inputs, production of organic detritus by phytoplankton, and benthic communities all interact to drive clarity patterns, with high spatial variability. Trends over the last 50 years show improvement, though with a temporary increase in organic detritus in response …

Using Remote Sensing Technology To Develop A Framework For Improving Hydrologic Models, Marissa Cook

Theses, Dissertations and Capstones

With increased storm intensity due to climate change and urbanization, flash flooding has become an increasingly significant issue globally and regionally. Although the factors influencing urban flash flooding are well-known, there is a growing need for technology to accurately and remotely predict the chance of a flash flood occurring from any given rain event to give people time to prepare. This study aims to use multispectral satellite imagery to provide a framework for improving near real-time flood predictions in an urban area of a high gradient, fourth order stream impacted by flooding. Specifically, we utilize satellite imagery to create the …

A Geospatial Assessment Of Groundwater Salinization In A Multi-Aquifer System: Durango, Mexico, Juan Lopez-Sierra

Graduate Theses/Dissertations

Groundwater salinization poses a critical environmental concern for water resource sustainability in arid and semi-arid regions. This study evaluates spatial and temporal patterns of groundwater salinity across the state of Durango, Mexico, using total dissolved solids (TDS), sodium adsorption ratio (SAR), as salinity indicators and nitrate-nitrogen (NO₃–N) as an anthropogenic indicator. Groundwater quality data were obtained from (CONAGUA), a Mexican water agency. To assess salinity variations with respect to time, while minimizing interannual sampling bias, two multi-year sampling periods were selected: 2012-2013, and 2020-2021. Final datasets consisted of 122 wells for 2012–2013 and 131 wells for 2020–2021. The wells were …

The Effect Of Incentives On Disaster Mitigation Behavior: An Age-Based Analysis, Wenqian He, Jeffrey Wickliffe, Zhen Cong

Health Sciences and Kinesiology Faculty Articles

Background

As climate change accelerates, the frequency and severity of natural disasters are increasing. However, most individuals cannot get sufficient protective measures due to financial pressure or environmental barriers. This study aims to examine how different incentives influence people’s willingness to take mitigation behavior. Methods

Data were collected from 781 tornado survivors in Texas, Alabama, and Tennessee, as part of the ‘Vulnerability and Resilience to Disasters’ project. Participants were randomly assigned to 12 conditions based on cost coverage ratios (25%, 50%, 75%), improvement types (storm shelters, structural reinforcement), and incentive forms (cash rebates, insurance discounts). Multivariate logistic regression models were …

A Half Century Of Biophysical Change In Polygonized Tundra On The Coastal Plain Of Northern Alaska, Mariana Mora

Open Access Theses & Dissertations

Climate change is amplified in the Arctic. As a result, Arctic tundra landscapes are undergoing physical changes through accelerated thaw and ice-wedge degradation. Understanding the magnitude and direction of these changes in the Arctic is a necessary step toward understanding the response of the Arctic to climate change and feedbacks to the Climate System. Through repeat measurements collected from 1973 through 2018, we were able to assess decadal time scale changes in polygon geomorphology, thaw depth, land surface compression and expansion, and vertical elevation within a highly polygonized tundra system, and their implications when scaled to a regional level. This …

Climate Change Has Increased Global Evaporative Demand Except In South Asia, Saeed Karimzadeh, Arman Ahmadi, Dennis Baldocchi, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Climate change alters how strongly the atmosphere draws water from the land, yet a consistent global assessment of this evaporative demand has been lacking. Here, we analyze 45 years of climate data and global models to quantify trends in the key drivers—air temperature, humidity, radiation, wind speed, and cloud cover—that determine the atmosphere’s drying power. We find that evaporative demand has increased worldwide, indicating a stronger atmospheric thirst, except in South Asia, where it has declined. There, widespread irrigation has increased soil and air moisture, enhanced cloud formation, and reduced sunlight reaching the surface, counteracting the global signal. These contrasting …

Locations Of Outfalls In The Indian River Lagoon And Lake Worth Lagoon, Florida, Kelly M. Kibler, Melinda J. Donnelly, Vincent Encomio, Madison Giuntoli, Manisha Thenuwara, Brice Bennett, Laura Andrade Barron, Mariam Sonbol, Wynter Raine Seppala, Namritha Ramakrishnan, Jennifer Jordan Báez, Beth Orlando, Irene Arpayoglou, Melissa Meisenburg

Indian River Lagoon Shorelines

The spatial dataset: UCF_Outfalls_IRL_LWL.shp was created under grants from the Indian River Lagoon National Estuaries Program and Florida Sea Grant. These projects cumulatively led to creation of a living shoreline restoration prioritization model and hydrodynamic habitat suitability models for 1,550 km (963 miles) of estuarine shorelines in Indian River Lagoon (IRL) and Lake Worth Lagoon (LWL).

The dataset presented here includes the location of outfalls identified throughout the study region and general characteristics of the shoreline where they were identified.

Seagrass Persistence In The Indian River Lagoon And Lake Worth Lagoon, Florida, Kelly Kibler, Melinda Donnelly, Vincent Encomio, Madison Giuntoli, Manisha Thenuwara, Brice Bennett, Laura Andrade Barron, Mariam Sonbol, Wynter Raine Seppala, Namritha Ramakrishnan, Jennifer Jordan Báez, Beth Orlando, Irene Arpayoglou, Melissa Meisenburg

Indian River Lagoon Shorelines

The spatial dataset: UCF_seagrasspersistence_IRL_LWL.shp was created with support from the Indian River Lagoon National Estuaries Program and Florida Sea Grant. These projects cumulatively led to creation of a living shoreline restoration prioritization model and hydrodynamic habitat suitability models for 1,550 km (963 miles) of estuarine shorelines in Indian River Lagoon (IRL) and Lake Worth Lagoon (LWL). The dataset presented herein represents persistence of seagrass throughout waters of the IRL and LWL.

Resource Assessment Report No.6: Western Rock Lobster Resource - 2025 Update Assessment, Simon De Lestang, Emma-Jade Tuffley

Resource Assessment Reports

Executive Summary

The western rock lobster (WRL) fishery is considered sustainable with catches being slightly below those associated with the maximum economic yield (MEY) proxy (39% harvest rate), which ensures the large lobster biomass and economical catch rates are maintained. The marine environment continues to be the biggest driver in stock dynamics with post larval recruitment (puerulus) and adult behaviour, including catchability, strongly influenced by oceanic conditions. Recent seasons have seen strong Leeuwin Currents and warm ocean conditions. Over the past few seasons, puerulus settlement levels have been below average at numerous locations and it has been almost 10 years …

Living Shoreline Prioritization And Hydrodynamic Habitat Suitability Models For Restoration In The Indian River Lagoon And Lake Worth Lagoon, Florida, Kelly M. Kibler, Melinda J. Donnelly, Vincent Encomio, Madison Giuntoli, Manisha Thenuwara, Brice Bennett, Laura Andrade Barron, Mariam Sonbol, Wynter Raine Seppala, Namritha Ramakrishnan, Jennifer Jordan Báez, Beth Orlando, Irene Arpayoglou, Melissa Meisenburg

Indian River Lagoon Shorelines

The spatial dataset: UCF_shorelinemodel_IRL_LWL.shp, was created with support from the Indian River Lagoon National Estuaries Program and Florida Sea Grant. These projects cumulatively led to creation of a living shoreline restoration prioritization model and hydrodynamic habitat suitability models for 1,550 km (963 miles) of estuarine shorelines in Indian River Lagoon and Lake Worth Lagoon.

The dataset presented herein includes:

1. shoreline data from >15,000 transects collected in the field,
2. shoreline wave climate data created through hydrodynamic modeling and frequency analysis of historic data,
3. categorization of potential risk for shoreline boat wake impact,
4. persistence of seagrass within 210 m of the shoreline, …

Quantifying Single, Compound And Cascading Climate Extremes: Implications For Agricultural Resilience In California, Shahryar Fazli, Wenzhao Li, Rejoice Thomas, Surendra Maharjan, Mohammad Sina Jahangiri, Andre Daccache, Hesham Morgan, Mohamed Allali, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

As climate change intensifies, extreme weather increasingly threatens California’s Central Valley (CV), a vital agricultural region exposed to rising risks from heatwaves (HW), droughts (DR), and compound extremes. These events disrupt crop productivity and broader processes like water demand, pest dynamics, and soil stability, posing systemic risks. This study examines the spatiotemporal dynamics of HW, coldwaves (CW), DR, excessive rainfall (ER), and their compound (e.g., HWDR) and cascading forms from 1951 to 2025, using NOAA nClimGrid-Daily data. We assessed trends in frequency, intensity, and duration over long-term (1951–2025) and mid-term (1981–2025) periods. Results show increasing HW and DR in the …

A Feature Engineering Technique For Enhancing The Generalization Of Machine Learning Models In Estimating Crop Evapotranspiration, Gaku Yokoyama, Sohta Harigai, Shigehiro Kubota, Koichi Nomura, Gregory R. Goldsmith, Daisuke Yasutake, Tomoyoshi Hirota, Masaharu Kitano

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurate and precise estimation of evapotranspiration (ET) is crucial for understanding the terrestrial carbon, water, and energy cycles. While process-based models of ET, such as the Penman–Monteith model offer robust generalization capabilities, they are limited by the need for detailed parameters (e.g., stomatal conductance,) that are challenging to measure continuously. On the other hand, machine learning models can estimate ET by capturing relationships between ET and environmental variables without experimentally measuring model parameters. However, machine learning models face the challenge of limited generalizability. This issue is particularly significant given the uncertainty introduced by changing climatic …

Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Ensuring global food security in the face of climate change requires optimizing crop water use and nutrient management. This study investigates the relationship between canopy nitrogen (N) and evapotranspiration (ET) across sunflower, rice, walnut, alfalfa, and plum crops using advanced remote sensing technologies. High-resolution hyperspectral data from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) and thermal multispectral data from the Landsat-based OpenET system were analyzed over 1,135 km² in California. Regression analysis revealed strong spatial association between canopy N and ET for sunflower (R² = 0.82), rice (R² = 0.71), and walnut (R² = 0.68), …

Warming Induces Unexpectedly High Soil Respiration In A Wet Tropical Forest, Tana E. Wood, Colin Tucker, Aura M. Alonso-Rodríguez, M. Isabel Loza, Iana F. Grullón-Penkova, Molly A. Cavaleri, Christine S. O'Connell, Sasha C. Reed

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Tropical forests are a dominant regulator of the global carbon cycle, exchanging more carbon dioxide with the atmosphere than any other terrestrial biome. Climate models predict unprecedented climatic warming in tropical regions in the coming decades; however, in situ field warming studies are severely lacking in tropical forests. Here we present results from an in situ warming experiment in Puerto Rico, where soil respiration responses to ⁺4 ^oC warming were assessed half-hourly for a year. Soil respiration rates were 42-204% higher in warmed relative to ambient plots, representing some of the highest soil respiration rates reported for any …

The Future Intensification Of Hydrological Extremes And Whiplashes In The Contiguous United States Increase Community Vulnerability, Surendra Maharjan, Wenzhao Li, John D. Bolten, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hydroclimatic whiplash rapid shifts between drought and flood poses growing risks to U.S. communities. Here, we assess historical extremes and future projections using a normalized streamflow metric: the annual mean flow’s deviation from the 1981–2020 average, expressed as a fraction of that average. This metric is applied to United States Geological Survey records and Localized Constructed Analogs downscaled projections under Representative Concentration Pathways 4.5 and 8.5. Results reveal sharp regional disparities, with drought deficits exceeding 300% of normal flow during multi-year droughts. By linking hydrologic outcomes with the Federal Emergency Management Agency’s National Risk Index, we find that counties facing …