Environmental Indicators and Impact Assessment Commons^™

Broadening The Community Of Nudibranch Enthusiasts Through Multilingual Educational Programming To Increase Climate Advocacy Engagement, Richelle Li Tanner, Cintya Felix Mendivil, Ashley Lam, Lorena Muñoz, Micah Kim, Elena G. Morales Poot, Gabrielle Keeler-May

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Environmental literacy and advocacy for environmental protections are essential components of responsible stewardship. For coastal communities, stewardship requires knowledge of ocean processes and the biological communities living within these ecosystems. While many studies highlight difficulties in engaging American audiences in environmental concern and stewardship due to hyper-individualistic societal values, tidepooling is one recreational pathway to coastal community engagement that strengthens sense of place, and therefore, responsibility to protect natural resources. We sought to broaden the tidepooling community to include more diverse voices through a participatory science program with inland city-dwelling, multilingual, and non-English speaking adults. Using nudibranchs as an environmental …

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 …

Public Responses About Air Quality In The World’S Ten Most Populous Countries, Noah Lim, Alessandro Del Ponte, Lina Ang, Wei Jie Seow

Political Science Faculty Articles and Research

Understanding how people view and react to outdoor air quality is crucial to effectively managing and responding to air pollution, which is a major threat to global public health. Using a quota sample of 10,618 respondents from 1,372 localities across the world’s ten most populous countries (Bangladesh, Brazil, China, Indonesia, India, Mexico, Nigeria, Pakistan, Russia, and the United States), we assessed the association between exposure to fine particulate matter with diameter ≤ 2.5 micrometers (PM2.5) and people’s knowledge, attitudes, perceptions, practices, and concern about air quality. Overall, we find an inverse association between air pollution exposure and knowledge about air …

Ecorxchoice.Com— An Sidp-Funded Calculator Integrating Environmental Metrics With Antimicrobial Stewardship, Pamela S. Lee, Hugh Gordon, Theresa Ferguson, Tien Dinh, Misty Vu, Marina Nguyen, Chaynor Hsiao, Gary Fong

Pharmacy Faculty Articles and Research

Healthcare sustainability is a multidisciplinary field that seeks to mitigate healthcare’s environmental consequences. Antimicrobial stewardship and healthcare sustainability both aim to reduce wasteful resource use while maximizing patient safety. However, a strong partnership between antimicrobial stewardship and healthcare sustainability has yet to develop. To facilitate environmentally sustainable decision-making in antimicrobial prescribing, we developed a web-based calculator (EcoRxChoice (www.ecorxchoice.com)) where users can enter different antimicrobial regimens and compare how much plastic waste each regimen creates. This work was supported in part by the Society of Infectious Diseases Pharmacists (SIDP), reflecting SIDP’s commitment to strengthening sustainability as a practical extension of antimicrobial …

Assessing Indoor Heat Vulnerability And Cooling Strategies In Three Mobile Home Communities In Boulder, Colorado, Usa, Mehdi P. Heris, Skye Niles, Alana Wilson, Megan Mccurdy, Amy Lacourse, Nick Lankau

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Extreme heat is an increasing public health and environmental threat shaped by both physical and social factors. This study assesses the vulnerability of mobile homes and their residents at three mobile home parks in Boulder, Colorado. We examined indoor temperature, housing conditions, and social and personal risk factors of the households. We monitored indoor and outdoor temperatures using 27 indoor and 5 outdoor temperature data loggers, comparing mobile homes with air conditioning, swamp coolers, and passive cooling designs. This work provides a detailed analysis of indoor temperature patterns and demonstrates how evaluating trends yields insights into the performance and effectiveness …

Urban Conflagrations: Structural Ash And Soil Metal(Loid) Contamination After California’S Eaton And Palisades Fires, S. Anselme Dossou, Leon Kelly, P. Louis Lu, Robin Jones, Oliver O'Donnell, Justin St. P. Walsh, Daniel D. Richter

Art Faculty Articles and Research

In the weeks after California’s Eaton-Palisades wildfires, structural ash of burned homes and soils from yards and curbsides were sampled for analysis of Pb, As, and eight metals from 32 burned residences. About six months after fires and after US Army Corps of Engineers’ Phase 2 cleanups removed 15 cm from structural ash footprints, 17 properties were resampled within about a meter of original sampling sites. Concentrations of metal(oid)s in structural ash and residential soils varied between and within properties with significant spatial and temporal patterns. Lead concentrations were highest in structural ash and soil of yards of select pre-1970s …

Monitoring Koyna Dam Displacements Using Persistent Scatterer Interferometry, Sara Zouriq, Gehan Hamdy, Amr Fawzy, Rejoice Thomas, Hesham El-Askary, Eehab Khalil, Mohamed Elsayad, Tarik El-Salawaky

Mathematics, Physics, and Computer Science Faculty Articles and Research

Monitoring dam stability is critical to ensure structural safety and operational reliability. This study integrates Persistent Scatterer Interferometry (PSI) based on Sentinel-1 SAR imagery (2020–2023) with Finite Element Method (FEM) simulations to assess the behavior of the Koyna Dam in India. PSI detected crest displacements between −1.0 and −1.8 mm yr−1, while FEM simulations predicted a maximum vertical displacement of approximately −3.2 mm at the crest. Although these results represent different quantities (time-averaged displacement rates versus peak static displacement), both approaches indicate millimeter-scale deformation and a consistent pattern of settlement at the dam crest, supporting the interpretation of hydrologically driven …

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 …

A Review Of Satellite-Derived Terrestrial Evapotranspiration: Theories, Methods And Products, Yunjun Yao, Jiquan Chen, Joshua B. Fisher, Changliang Shao, Yuanbo Liu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurately estimating terrestrial evapotranspiration (ET), the second-largest hydrologic flux in the terrestrial water cycle, is vital for understanding global water and carbon exchanges. It is difficult to measure and estimate terrestrial ET at regional and global scales. Satellites have provided us an effective tool to estimate regional and global terrestrial ET in recent decades. In this article, we provide a comprehensive review of the basic theoretical foundations, methods and products of satellite-derived terrestrial ET. The basic theoretical foundations for estimating terrestrial ET are the Monin–Obukhov similarity theory (MOST) and other budding theories (e.g., Maximum entropy production theory, and generalized Hamilton …

Thermal Inequities In Public Parks And Open Spaces In Los Angeles Determined By Remote Sensing, Ashley Agatep, Joshua B. Fisher, Kainani Tacazon, Ambar Rivera, Rossmery Zayas, Reginald Archer, Juan Carlos Ruiz Malagon, Jason A. Douglas

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Urban heat island (UHI) effects are amplified by disparities in community-level cooling infrastructure—such as urban public parks and open spaces (PPOS)—with the most severe impacts occurring in communities that lack sufficient city-wide cooling resources. We found that the underserved community of South Los Angeles, as compared to their neighboring West Los Angeles counterparts, suffers a double burden of inequitable (1) access to and (2) absence of cooling materials and surfaces within these spaces, resulting in significantly hotter temperatures. Identifying these inequities requires high-resolution temporal and spatial mapping of surface temperatures, which has been previously limited. In this community-based participatory research …

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 …

The World's Largest Saddle Dam At Risk: Multisensor Geohazard Analysis And Downstream Impacts, Hesham El-Askary, Hesham Morgan, Surendra Maharjan, Ali Elgendy, Wenzhao Li, Rejoice Thomas, Austin Madson, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Grand Ethiopian Renaissance Dam (GERD) Saddle Dam, which holds approximately 89% of the main reservoir's live storage, is one of the largest and most critical auxiliary dams globally; its construction on Ethiopia's Blue Nile has consequently raised significant regional and international concerns regarding potential environmental impacts and geohazard risks. This study presents a comprehensive risk assessment of the GERD Saddle Dam by integrating high-resolution satellite data (GRACE, Sentinel-1, Sentinel-2, WorldView-3), hydrological modeling (SWAT), Persistent Scatterer Interferometry (PSI), geospatial analysis, and advanced statistical techniques. The results highlight critical structural vulnerabilities, including groundwater infiltration estimated at approximately 41 ± 6.2 billion …

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, …

High Spatiotemporal Resolution Monitoring Of Crop Water Stress Across The Contiguous United States Using Harmonized Landsat And Sentinel-2 Data, Na Chen, Yanlei Feng, Na Wang, Jevan Yu, Mohammad Reza Alizadeh, Yifeng Cui, Ning Ye, Wenzhe Jiao, Joshua B. Fisher, César Terrer

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurate and timely monitoring of crop water stress is essential for efficient agricultural water management, ultimately maintaining and improving crop productivity. While Landsat has been used for this purpose, its temporal resolution hampers timely detection of crop water stress. The recently released Harmonized Landsat and Sentinel-2 Version 2.0 dataset, which enables a higher-frequency time series of satellite observations (2–3 days, 30 m), offers a promising solution to this challenge. However, its potential for crop stress monitoring remained unexplored. In this study, we utilized 923 HLS satellite tiles to assess crop water stress across the contiguous United States (CONUS). Crop water …

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 …

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 …

Harnessing Hyperspectral Imaging And Deep Learning For Terrestrial Habitat Mapping In Arid Landscapes: A Case Study In Saudi Arabia, Ali Elgendy, Hesham Morgan, Brandon Tran, Rejoice Thomas, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Arid ecosystems remain under-mapped at actionable scales despite their ecological importance. Decision makers lack reliable, high-resolution habitat maps in drylands to prioritize protection and target restoration. This research integrates spaceborne hyperspectral imaging from the Environmental Mapping and Analysis Program (EnMAP) with deep learning semantic segmentation models to produce an updated level of habitat classification based on the International Union for Conservation of Nature (IUCN) for part of the Imam Turki bin Abdullah Royal Reserve, Saudi Arabia. Using ground control points and the full EnMAP spectral cube without band selection, U-Net and DeepLabV3+ architectures were each implemented with VGG19 and ResNet-101 …

Detecting Burned Vegetation Areas By Merging Spectral And Texture Features In A Resnet Deep Learning Architecture, Jiahui Fan, Yunjun Yao, Yajie Li, Xueyi Zhang, Jiquan Chen, Joshua B. Fisher, Xiaotong Zhang, Bo Jiang, Lu Liu, Zijing Xie, Luna Zhang, Fei Qiu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Timely and accurate detection of burned areas is crucial for assessing fire damage and contributing to ecosystem recovery efforts. In this study, we propose a framework for detecting fire-affected vegetation anomalies on the basis of a ResNet deep learning (DL) algorithm by merging spectral and textural features (ResNet-IST) and the vegetation abnormal spectral texture index (VASTI). To train the ResNet-IST, a vegetation anomaly dataset was constructed on high-resolution 30 m fire-affected remote sensing images selected from the Global Fire Atlas (GFA) to extract the spectral and textural features. We tested the model to detect fire-affected vegetation in ten study areas …

Dramatic Biases In Terrestrial Nitrogen Fixation In Earth System Models Revealed By Natural Isotope Signatures, Maoyuan Fang, Shushi Peng, Philippe Ciais, Daniel S. Goll, Benjamin Z. Houlton, Ying-Ping Wang, Yilong Wang, Pan Liu, Joshua B. Fisher, Pierre Regnier

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Biological nitrogen fixation (BNF) is the primary input of new reactive nitrogen to natural terrestrial ecosystems. However, this flux is poorly constrained due to its unclear drivers and associated control mechanisms. Here, we extend the existing theory of nitrogen (N) isotope mass balance to estimate BNF rates and then use a Bayesian approach to constrain the BNF rates in natural terrestrial ecosystems by using measurements of natural N-isotope ratios (δ¹⁵N) in plants (δ_P) and soil (δ_S). Together with pairwise δ_P and δ_S measurements from 18 forest sites covering diverse climates and thousands …

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 …

Air Pollution And Diseases: Signaling, G Protein-Coupled And Toll Like Receptors, Isabella Cattani-Cavalieri, Katrina F. Ostrom, Jordyn Margolis, Rennolds S. Ostrom, Martina Schmidt

Pharmacy Faculty Articles and Research

Air pollution is a significant public health issue that impacts lung health, particularly in vulnerable populations such as children, the elderly, and individuals with pre-existing respiratory conditions. Both natural and anthropogenic sources of air pollution give rise to a variety of toxic compounds, including particulate matter (PM), ozone (O₃), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and polycyclic aromatic hydrocarbons (PAHs). Exposure to these pollutants is strongly associated with the development and exacerbation of respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), lung cancer, and idiopathic pulmonary fibrosis (IPF). Notably, early life exposure to pollutants such as …

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 …