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 …

Global Performance Of Remote Sensing-Based And Reanalysis-Driven Models To Estimate Open Water Evaporation, Júlia Brusso Rossi, Ayan Santos Fleischmann, Leonardo Laipelt, Bruno Comini De Andrade, Joshua B. Fisher, Justin L. Huntington, Christopher Pearson, R. Iestyn Woolway, Roseilson Vale, Júlio Tota, Gabriel B. Senay, Huilin Gao, Anderson Ruhoff

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, we analyze the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. We compare 3 remote sensing‐based …

Hydroclimatic Whiplash Across The Contiguous United States: Characterizing Wet–Dry Transitions In Pdsi And Phdi, James Lam, Thomas Christopher Piechota

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Hydroclimatic whiplash, characterized by rapid transitions between wet and dry conditions, poses a growing challenge for water resource management and ecosystem stability. However, the spatial and statistical characteristics of wet–dry transitions across the contiguous United States are not fully understood. This study analyzed Palmer Drought Severity Index (PDSI) and Palmer Hydrological Drought Index (PHDI) records from 344 NOAA climate divisions spanning 1895–2024. Whiplash was defined as a transition between severe drought and severe wetness within 12-month rolling windows. Statistical descriptors including lag-1 autocorrelation, variance, and skewness were evaluated for whiplash and non-whiplash windows to assess differences between them. Whiplash hotspots …

A Copula-Based Framework For Multivariate Count Time Series With Mixed Marginal Distributions, Dimuthu Fernando, Yuxin Wen, Wimarsha Jayanetti

Engineering Faculty Articles and Research

We developed a class of multivariate integer-valued time series models using copula theory. Each count time series is modeled as a Markov chain, with serial dependence characterized through copula-based transition probabilities for Poisson and negative binomial marginals. Cross-sectional dependence is modeled via a trivariate Gaussian or a “t-copula”, allowing for both positive and negative correlations and providing a flexible dependence structure. Model parameters are estimated using likelihood-based inference, where the trivariate Gaussian or t-copula integrals are evaluated through standard randomized Monte Carlo methods. Simulation results, along with an analysis of annual counts of major hurricanes (Category 3+) across the North …

Effects Of Warming On Growth And Leaf Colonization By Litter Mat-Forming Fungi In A Wet Tropical Forest In Puerto Rico, Ari E. Puentes, D. Jean Lodge, Deyaneira A. Ortiz-Iglesias, Tatiana Barreto-Vélez, Laura C. Rubio-Lebrón, Hieu Chu, Christine S. O'Connell, Sasha C. Reed, Tana E. Wood

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wet tropical forests are experiencing rising temperatures and increased frequency and intensity of extreme climatic events, such as cyclones, which can increase rates of soil erosion and surface runoff. Fungal litter mats, formed by agaric decomposer fungi, play a crucial role in stabilizing slopes, preventing erosion, and aiding nutrient cycling; however, little is known about how warming affects litter mat growth and function. We investigated two litter mat-forming fungi, Gymnopus johnstonii and Marasmius aff. crinis-equi, in warmed (+4°C above ambient) and control plots in the Luquillo Experimental Forest, Puerto Rico. Growth and time-to-leaf colonization were monitored over 6 weeks …

Lowland Tropical Forests Remain A Methane Sink Under Warming And Long-Term Hurricane Disturbance Recovery, Gabriele Larocca Conte, Lucia Zuvela, Rachel Cruz-Pérez, Tatiana Barreto-Vélez, Nibia Becerra-Santillan, Sophia F. Campbell, Hieu P. Chu, Trung Dam, Iana F. Grullón-Penkova, Miriam Kleit, Deyaneira A. Ortiz-Iglesias, Laura C. Rubio-Lebrón, Molly A. Cavaleri, Sasha C. Reed, Debjani Sihi, Tana E. Wood, Christine S. O'Connell

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Methane (CH₄) is a potent greenhouse gas, and tropical forests account for roughly one–third of global atmospheric CH₄ uptake by soils. Projected warming and more frequent hurricanes in these ecosystems may alter soil CH₄ sink strength, as warmer and wetter soils enhance methanogenesis activity. We measured soil CH₄ and CO₂ efflux during the calendar summer months of 2023 and 2024 alongside continuous records of soil moisture, soil and air temperature, and precipitation in an in–situ warming experiment (TRACE) located in a lowland tropical forest in Puerto Rico, six to seven years after Hurricanes Irma …

Evapotranspiration Everywhere, All The Time: Towards A Unified View From Earth Observation, Joshua B. Fisher, Martha C. Anderson, Diego G. Miralles, Kanishka Mallick, Paul C. Stoy, Youngryel Ryu, Wim G. M. Bastiaanssen

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Scientists want to know everything, everywhere, and all the time. This is particularly true in Earth science, where we seek to understand processes that span from the molecular to the planetary scale in how the world works, how it affects us, and how we impact it—especially the water cycle. Evapotranspiration (ET) was the last component to be measured in closing the water cycle: for decades, closing the water budget meant adding up all the measurable components, then inferring ET as the residual. Early measurements relied on water loss from pans and weighing lysimeters, followed by sensors inserted into plants to …

Warming Climate Amplifies Vapor Pressure Deficit Limits On Gross Primary Productivity, Shiqin Xu, Nate G. Mcdowell, Tim R. Mcvicar, Diego G. Miralles, Stephen Sitch, Pablo Sanchez-Martinez, Joshua B. Fisher, Pierre Friedlingstein, Hylke E. Beck, Matthew F. Mccabe

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Ongoing climate warming may profoundly impact terrestrial gross primary productivity (GPP), a key component of the global carbon cycle. However, uncertainty in the relative roles of atmospheric water demand (vapor pressure deficit, VPD) and root-zone soil moisture (SM) limits predictions of drought impacts on GPP. Here, we show that growing-season GPP was more strongly constrained by VPD than SM globally, based on observation-constrained model estimates, satellite retrievals and Dynamic Global Vegetation Model simulations. The importance of VPD increased with higher temperatures and more severe and prolonged droughts. This pattern reflects VPD’s critical role in regulating stomatal conductance and plant hydraulic …

Fear In The Forecast: Do You Believe The Government Controls The Weather?, Gabriella Bartsch

Honors Papers and Posters

Weather manipulation by the government is among the most widely believed conspiracy theories in America, with over 49 percent of Americans subscribing to this belief. As belief in weather-related conspiracies continues to gain traction, understanding the political and social factors that drive these beliefs becomes increasingly essential. In this study, data from the Chapman Survey of American Fears, a representative national sample of U.S. adults, as well as a qualitative content analysis of X/Twitter posts from 2020 to 2026, are used. This research examines how political partisanship and conspiratorial predisposition are reflected in media consumption and levels of institutional trust, …

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 …

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 …

Deep Learning Style Transfer For Enhanced Smoke Plume Visibility: A Standardized False Color Composite (Sfcc) In Gems Satellite Imagery, Yemin Jeong, Seung Hee Kim, Menas Kafatos, Jeong-Ah Yu, Kyoung-Hee Sung, Seung-Yeon Kim, Goo Kim, Jae-Jin Kim, Yangwon Lee

Institute for ECHO Articles and Research

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr2, and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, …

A High-Resolution Daily Precipitation Fusion Framework Integrating Radar, Satellite, And Nwp Data Using Machine Learning Over South Korea, Hyoju Park, Hiroyuki Miyazaki, Menas Kafatos, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover …

Observation-Based Reconstruction Of High-Resolution Daily Temperature Field Using Lapse-Rate-Constrained Kriging In Complex Terrain: A Nationwide Dataset For South Korea, Youjeong Youn, Menas Kafatos, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

High-resolution air-temperature fields are essential for climate, hydrologic, and ecological applications in complex terrain, yet operational products often lack the spatial detail to resolve topographic effects. We develop an observation-driven reconstruction of daily air temperature fields for South Korea (2024) using ordinary kriging with lapse-rate correction (OKLR), integrating a dense network of over 500 stations from the Automatic Mountain Meteorology Observation System (AMOS) and the Automated Surface Observing System (ASOS). The OKLR framework systematically removes elevation-driven trends using a physically based fixed lapse rate (–6.5 °C km−1), performs kriging on detrended residuals, and reapplies Digital Elevation Model (DEM)-based corrections to …

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, …

Synthesis Study Of Evapotranspiration Evolution In The Mekong Delta Induced By Land Use And Land Cover Changes In The Decades Of 1990–2020, Emiliana Valentini, Serena Sapio, Son V. Nghiem, Seung Hee Kim, Sara Liburdi, Andrea Taramelli

Institute for ECHO Articles and Research

Evapotranspiration (ET) plays a key role in the water cycle and balance, and its estimation is of paramount importance in hydrological studies. This variable is also strongly influenced by the land use and land cover (LULC). This study use a synthesis approach to analyse the relationship between ET variation and LULC transformation in the period 1990–2020. The study area represents a major agricultural region in the Mekong Delta of Vietnam between the Mekong and Bassac rivers. The multivariate dataset was ingested into the hydrological model Soil and Water Assessment Tool to examine the multidecadal ET evolution across the study region. …

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 …

Real-Time Production Of High-Resolution, Gap-Free, 3-Hourly Aod Over South Korea: A Machine Learning Approach Using Model Forecasts, Satellite Products, And Air Quality Data, Seoyeon Kim, Youjeong Youn, Menas Kafatos, Jaejin Kim, Wonsik Choi, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Aerosol optical depth (AOD) is essential for air quality monitoring and climate research. However, satellite-based retrievals suffer from cloud-related data gaps, and reanalysis products are limited by coarse spatial resolution and substantial production latency. This study develops a real-time, gap-free, high-resolution (1.5 km) AOD retrieval system for South Korea. The system integrates Copernicus Atmosphere Monitoring Service (CAMS) forecasts, high-resolution meteorological fields, and ground-based air quality observations within a machine learning framework. Three models with varying training periods were systematically evaluated using cross-validation and independent validation with 2024 Aerosol Robotic Network (AERONET) data. The optimal model, trained on 2015–2023 data, achieved …

Multi-Satellite Image Matching And Deep Learning Segmentation For Detection Of Daytime Sea Fog Using Gk2a Ami And Gk2b Goci-Ii, Jonggu Kang, Hiroyuki Miyazaki, Seung Hee Kim, Menas Kafatos, Daesun Kim, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher …

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 …

Direct And Indirect Effects Of Water-Table Levels On Redox-Active Organic Matter Reduction In An Alaskan Rich Fen, J. E. Rush, E. S. Kane, Jason K. Keller, J. C. Bowen, Cassandra A. Zalman, E. S. Euskirchen, K. H. Wyatt, A. R. Rober, E. S. Hinckley

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Redox-active organic matter (RAOM) reduction is an important control on methane production in northern peatlands, but it is unclear how global climate change will affect RAOM reduction. We investigated the effects of water-table levels on RAOM reduction by leveraging a long-term water-table manipulation experiment in an Alaskan fen, which includes Lowered and Raised treatment plots relative to a Control. Common substrate peat was incubated in each plot during one summer of experimental manipulation and another summer of site-wide flooding. During experimental manipulation, common substrate RAOM was more reduced in the Raised plot than the Lowered plot at both 10–20 cm …

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 …