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Articles 31 - 60 of 232

Full-Text Articles in Environmental Indicators and Impact Assessment

Performance Mapping And Weighting For The Evapotranspiration Models Of The Openet Ensemble, M. Reitz, J. M. Volk, T. Ott, M. Anderson, G. B. Senay, F. Melton, A. Kilic, R. Allen, Joshua B. Fisher, A. Ruhoff, A. J. Purdy, J. Huntington Aug 2025

Performance Mapping And Weighting For The Evapotranspiration Models Of The Openet Ensemble, M. Reitz, J. M. Volk, T. Ott, M. Anderson, G. B. Senay, F. Melton, A. Kilic, R. Allen, Joshua B. Fisher, A. Ruhoff, A. J. Purdy, J. Huntington

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Evapotranspiration (ET) accounts for the majority of water available from precipitation in the terrestrial water cycle, and improvements to the accuracy, resolution, and coverage of ET data can enhance hydrologic models and assessments. The OpenET collaboration of six remotely sensed ET modeling teams has demonstrated that an ensemble approach to ET estimation generally provides improved accuracy relative to individual ensemble members. The performance of individual models has been shown to vary by land cover type and climate zone, but a thorough study of the variables that influence model performance differences has not yet been conducted. In this paper, we model …

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Assessing Coastal Vulnerability And Climate-Driven Migration Risk In West Africa, Wenzhao Li, Surendra Maharjan, Hesham El-Askary Aug 2025

Assessing Coastal Vulnerability And Climate-Driven Migration Risk In West Africa, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

This study presents a GIS-based multi-criteria decision-making framework to assess climate-induced migration risk along the West African coast. We developed a comprehensive risk index that integrates environmental hazards such as flood frequency and socio-economic vulnerability indicators, including poverty levels, population density, and adaptive capacity. By utilizing datasets such as the Geocoded Disasters (GDIS) dataset, Social Vulnerability Index (SVI), Poverty and Adaptive Capacity Index (PACI), and the Population Exposure Index (PEI), the study identifies regions most susceptible to displacement. Results reveal that areas like Benin’s Abomey-Calavi, Cotonou, and Akpo-Misserete are especially vulnerable due to high disaster frequency, substantial population exposure, and …

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Multi-Crop Systems And Crop-Switching Strategies To Enhance Water Use Efficiency And Climate Resilience In Arid Agricultural Regions, Shahryar Fazli, Wenzhao Li, Surendra Maharjan, Hesham El-Askary Aug 2025

Multi-Crop Systems And Crop-Switching Strategies To Enhance Water Use Efficiency And Climate Resilience In Arid Agricultural Regions, Shahryar Fazli, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Agriculture in the Lower Colorado River (LCR) region faces mounting challenges from climate change, arid conditions, and water scarcity. This study evaluates water use efficiency (WUEc) and crop-switching strategies under SSP2-4.5 and SSP5-8.5 scenarios for 2025–2049, 2050–2074, and 2075–2099. Using historical data, climatic drivers such as temperature and precipitation were analyzed for their influence on key crops, including durum wheat, winter wheat, and corn. Results show SSP2-4.5 supports water use reductions up to 16%, stable profits (80–90%), and modest calorie increases (up to 15%), while SSP5-8.5 poses severe challenges, with water use reductions of 2–5%, profits dropping to around 20%, …

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Wildfires Classification In Canadian Boreal Forest: A Comparative Study Of Logistic Regression And Xgboost Models, Brandon Tran, Elijah James Duran, Mike Luu, Hesham Morgan, Surendra Maharjan, Wenzhao Li, Hesham El-Askary Aug 2025

Wildfires Classification In Canadian Boreal Forest: A Comparative Study Of Logistic Regression And Xgboost Models, Brandon Tran, Elijah James Duran, Mike Luu, Hesham Morgan, Surendra Maharjan, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

In recent years, Canada has faced a growing number of wildfires. These events have devastated ecosystems, displaced communities, and posed severe health risks. To minimize the damage caused by such disasters, this study aims to develop an early warning system that predicts wildfire occurrences. Two machine learning models for binary classification of wildfire occurrence in Canadian wild forests, Logistic regression and XGBoost, will be compared and evaluated. The models are used to predict the likelihood of wildfire events based on various environmental and climatic factors. The models are evaluated using a 70-30 split validation approach and their performance is assessed …

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Decoding Teleconnection Impacts On Hydrological Switches In The Conus Using Wavelet Analysis, Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Hesham Morgan, Hesham El-Askary Aug 2025

Decoding Teleconnection Impacts On Hydrological Switches In The Conus Using Wavelet Analysis, Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Hesham Morgan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hydrometeorological teleconnections are key drivers of hydrological processes, representing the influence of large-scale atmospheric circulation patterns on regional climates. Understanding these teleconnections provides crucial insights into the mechanisms underlying hydrometeorological phenomena, particularly hydrological switches—rapid transitions between extreme events such as droughts and floods. These switches have become increasingly prevalent across the contiguous United States (CONUS), fueled by climate variability and evolving atmospheric patterns. This study utilizes cross-wavelet transform analysis to examine the spatial and temporal dynamics of hydrological switches and their correlations with major teleconnection indices, including NAO, ONI, WP, PDO, PNA, and QBO. The findings indicate significant coherence between …

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Insights From Swot Data On Transboundary Upstream-Downstream Impacts In The Nile Basin, Hesham Morgan, Wenzhao Li, Ali Elgendy, Surendra Maharjan, Rejoice Thomas, Hesham El-Askary Aug 2025

Insights From Swot Data On Transboundary Upstream-Downstream Impacts In The Nile Basin, Hesham Morgan, Wenzhao Li, Ali Elgendy, Surendra Maharjan, Rejoice Thomas, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

This study utilizes high-resolution data from NASA's Surface Water and Ocean Topography (SWOT) mission to investigate water dynamics and upstream-downstream impacts across key reservoirs in the Nile Basin. Focusing on the Grand Ethiopian Renaissance Dam (GERD), Rosaries Dam, Merowe Dam, and the Aswan High Dam, the analysis spans 15 months (August 2023 to October 2024). By systematically selecting 30 points across each reservoir, monthly boxplots of surface water elevation were generated, revealing significant temporal and spatial variability. The results show that GERD’s filling phase led to a steady increase in water levels (peaking at ~615 meters from June to October …

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Assessing Meteorological Impacts On Hydrological Switches In The Conus, Surendra Maharjan, Wenzhao Li, Sujan Shrestha, Hesham El-Askary Aug 2025

Assessing Meteorological Impacts On Hydrological Switches In The Conus, Surendra Maharjan, Wenzhao Li, Sujan Shrestha, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hydrological switches, defined as rapid transitions between extreme meteorological events such as droughts and floods, are becoming increasingly frequent across the contiguous United States (CONUS) due to climate variability. This study analyzes the spatial and temporal patterns of these hydrological switches and their correlation with large-scale meteorological indices, such as the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Streamflow data from the US Geological Survey (USGS) is analyzed to investigate the impact of meteorological drivers on hydrological variability. Results indicate that regions dependent on snowmelt exhibit delayed hydrological responses to climatic conditions, while areas in the Eastern …

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Adaptive Crop Switching For Irrigated Agriculture In Response To Climate Change In The Western U.S., Shahryar Fazli, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Aqil Tariq, Hesham El-Askary Jul 2025

Adaptive Crop Switching For Irrigated Agriculture In Response To Climate Change In The Western U.S., Shahryar Fazli, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Aqil Tariq, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Adaptive irrigation strategies are crucial for balancing water use, economic viability, and food security in the arid Western United States. However, as a key indicator vital in regulating agricultural productivity and crop irrigation, water use efficiency (WUEc) is becoming increasingly complex to estimate due to climate change. This study explores the critical role of key meteorological drivers, such as maximum temperature (tmax) and vapor pressure deficit (vpd), and their impacts on crop-specific WUEc. Future impacts are also assessed through integrating machine learning models with climate projections from the CMIP6 framework under SSP2–4.5 and SSP5–8.5 scenarios to forecast WUEc trends from …

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High Spatial Resolution Crop Type And Land Use Land Cover Classification Without Labels: A Framework Using Multi-Temporal Planetscope Images And Variational Bayesian Gaussian Mixture Model, Minh Tri Le Jul 2025

High Spatial Resolution Crop Type And Land Use Land Cover Classification Without Labels: A Framework Using Multi-Temporal Planetscope Images And Variational Bayesian Gaussian Mixture Model, Minh Tri Le

Mathematics, Physics, and Computer Science Faculty Articles and Research

Previous studies often combined high spatial resolution data (e.g., PlanetScope) with wider spectral range data (e.g., Sentinel-2) and relied on supervised classification methods to produce land use and land cover (LULC) maps. This study proposed a new unsupervised framework to generate crop type and LULC maps at high spatial resolution (< 5 m) using available PlanetScope data solely without requiring ground truths. We used PlanetScope surface reflectance images and their derived spectral indices during growing seasons to create multi-temporal input features, which were fed into an unsupervised Variational Bayesian Gaussian Mixture Model (VBGMM). The VBGMM, unlike the traditional unsupervised classification methods, (1) first estimated optimal parameters that are most suitable based on the input features and then (2) assigned pixels to the cluster with maximum posteriori probability of a mixture of several Gaussian distributions. The crop type and LULC maps were then generated by labeling the derived clusters using the best possible assignment method, referring to the existing crop type or LULC products. We evaluated the produced PlanetScope-based crop type and LULC maps using true labels, corresponding reference maps, and other unsupervised classification methods. The results demonstrated the robustness and effectiveness of the proposed framework in mapping crop types and LULC at 3–5 m pixels across various ecosystems, climate zones, and human-managed landscapes. The spatial patterns of PlanetScope-based maps were (1) highly comparable with all the reference datasets at 10–30 m spatial resolution and (2) better than the traditional GMM and K-means clustering methods. The VBGMM produced classification maps with high confidence, yielding class probabilities above 0.9 for over 90 % of all study areas. The area percentage for all crop type and LULC classes agreed well with their reference maps, with R2 of 0.95 and RMSE of 1.04 %. The confusion matrices using true labels indicated that PlanetScope-based maps achieved a higher overall accuracy of 84 % than the supervised referenced maps of 81 %. Besides, the entropy comparison showed that our framework-based maps were better at capturing fine-scale features such as developed areas within cities that commonly mix with open space and vegetation, deforestation and cropland conversion in South America, smallholder croplands in Africa and Asia, and generating homogeneous crop fields in North America. This study further highlighted the potential for future research to implement our proposed framework to generate timely and extensive annotated datasets, which can be used for operationally training machine learning models to map crop types and LULC, track deforestation, detect wildfires, and delineate flooded areas at larger scales using medium/coarse Earth observations.

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Hyperspectral Band Selection Via Heterogeneous Graph Convolutional Self-Representation Network, Junde Chen, Wenzhao Li, Surendra Maharjan, Hesham El-Askary Jul 2025

Hyperspectral Band Selection Via Heterogeneous Graph Convolutional Self-Representation Network, Junde Chen, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hyperspectral image (HSI) band selection (BS) plays a crucial role in HSI dimensionality reduction, aiming to identify a representative subset of bands with minimal redundancy. However, conventional BS approaches primarily operate in the Euclidean domain, often overlooking the structural characteristics of pixels and spectral bands, such as spatial continuity and spectral dependencies. In addition, they handle each HSI as an integrated unit to harness implicit spatial information, disregarding spatial distribution variations across different homogeneous regions. To fully leverage structural information, this study introduces a novel BS method, termed the dual heterogeneous graph convolutional network with enhanced self-representation (ESR-HGCN), for HSI …

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Mitigation Behaviors Of Homeowners And Renters In The Wildland Urban Interface, Aishwarya Borate, Omar Pérez Figueroa, Douglas Houston, Christopher Ihinegbu, Ariane Jong-Levinger, Jochen E. Schubert, Brett F. Sanders Jul 2025

Mitigation Behaviors Of Homeowners And Renters In The Wildland Urban Interface, Aishwarya Borate, Omar Pérez Figueroa, Douglas Houston, Christopher Ihinegbu, Ariane Jong-Levinger, Jochen E. Schubert, Brett F. Sanders

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Residential development within the wildland-urban interface (WUI) has greatly expanded in the United States since the 1990s, amplifying wildfire risk by placing people and structures in greater proximity to flammable vegetation. Household wildfire mitigation actions can vary substantially by cost, knowledge required, and perceived effectiveness, but few studies have examined them separately and how their adoption varies by housing tenure in the context of wildfires. To address this gap, we surveyed residents living in WUI areas within Southern California near recent burn scars in the Santa Ana and San Bernardino Mountain ranges. Drawing on the Protection Motivation Theory and the …

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Remote Sensing-Based Assessment Of Evapotranspiration Patterns In A Unesco World Heritage Site Under Increasing Water Competition, Maria C. Moyano, Monica Garcia, Luis Juana, Laura Recuero, Lucia Tornos, Joshua B. Fisher, Néstor Fernández, Alicia Palacias-Orueta Jul 2025

Remote Sensing-Based Assessment Of Evapotranspiration Patterns In A Unesco World Heritage Site Under Increasing Water Competition, Maria C. Moyano, Monica Garcia, Luis Juana, Laura Recuero, Lucia Tornos, Joshua B. Fisher, Néstor Fernández, Alicia Palacias-Orueta

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

In water-scarce regions, natural ecosystems and agriculture increasingly compete for limited water resources, intensifying stress during periods of drought. To assess these competing demands, we applied a modified PT-JPL model that incorporates the thermal inertial approach as a substitute for relative humidity (RH) in estimating soil evaporation—a method that significantly outperforms the original PT-JPL formulation in Mediterranean semi-arid irrigated areas. This remote sensing framework enabled us to quantify spatial and temporal variations in water use across both natural and agricultural systems within the UNESCO World Heritage site of Doñana. Our analysis revealed an increasing evapotranspiration (ET) trend in intensified agricultural …

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A Novel Approach To Increase Accuracy In Remotely Sensed Evapotranspiration Through Basin Water Balance And Flux Tower Constraints, Kul Khand, Gabriel B. Senay, Mackenzie Friedrichs, Koong Yi, Joshua B. Fisher, Lixin Wang, Kosana Suvočarev, Arman Ahmadi, Housen Chu, Stephen Good, Kanishka Mallick, Justine Missik, Jacob A. Nelson, David E. Reed, Tianxin Wang, Xiangming Xiao Jul 2025

A Novel Approach To Increase Accuracy In Remotely Sensed Evapotranspiration Through Basin Water Balance And Flux Tower Constraints, Kul Khand, Gabriel B. Senay, Mackenzie Friedrichs, Koong Yi, Joshua B. Fisher, Lixin Wang, Kosana Suvočarev, Arman Ahmadi, Housen Chu, Stephen Good, Kanishka Mallick, Justine Missik, Jacob A. Nelson, David E. Reed, Tianxin Wang, Xiangming Xiao

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address …

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Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros Jul 2025

Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wildfire prediction models that can be applied across diverse regions at fine scales (<  100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km2 from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R2 = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) …

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Enhancing Water Scarcity Resilience In Egypt Through Machine Learning-Driven Phenological Crop Mapping And Water Use Efficiency Analysis, Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Aqil Tariq, Rejoice Thomas, Cyril Rakovski, Hesham El-Askary Jun 2025

Enhancing Water Scarcity Resilience In Egypt Through Machine Learning-Driven Phenological Crop Mapping And Water Use Efficiency Analysis, Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Aqil Tariq, Rejoice Thomas, Cyril Rakovski, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Agriculture forms the backbone of Egypt’s economy, with the Nile Valley and Delta serving as key production zones for crops like wheat, rice, and clover. However, the sector faces mounting pressure from water scarcity, as it depends almost entirely on the Nile for irrigation, making it necessary to map major crops for assessing Water Use Efficiency (WUE) and informing agricultural planning. In this study, we used machine learning (ML) techniques—specifically Support Vector Machine (SVM) to time-series phenological data and optical indices (Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and …

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Evaluation Of Ecostress Collection 2 Evapotranspiration Products: Strengths And Uncertainties For Evapotranspiration Modeling, Zoe Amie Pierrat, Adam J. Purdy, Gregory Halverson, Joshua B. Fisher, Kanishka Mallick, Madeleine Pascolini-Campbell, Youngryel Rye, Martha C. Anderson, Claire Villanueva-Weeks, Margaret C. Johnson, Brenna Hatch, Evan Davis, Yun Yang, Kerry Cawse-Nicholson Jun 2025

Evaluation Of Ecostress Collection 2 Evapotranspiration Products: Strengths And Uncertainties For Evapotranspiration Modeling, Zoe Amie Pierrat, Adam J. Purdy, Gregory Halverson, Joshua B. Fisher, Kanishka Mallick, Madeleine Pascolini-Campbell, Youngryel Rye, Martha C. Anderson, Claire Villanueva-Weeks, Margaret C. Johnson, Brenna Hatch, Evan Davis, Yun Yang, Kerry Cawse-Nicholson

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) collects thermal observations from the International Space Station to support evapotranspiration (ET) research at fine spatial resolutions (70 m × 70 m). Initial ET from ECOSTRESS Collection 1 was used in scientific research and applications, though subsequent analyses identified areas for improvement. This study outlines updates to ECOSTRESS Collection 2 ET and presents an accuracy assessment of ET and auxiliary variables validated against in situ data from AmeriFlux. Key updates in Collection 2 include use of four independent model estimates of instantaneous latent energy (LE) and improved auxiliary forcing data. …

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Land Instability Compounds The Risk Of Sea Level Rise In Alexandria, Egypt, Rejoice Thomas, Sara Zouriq, Shahryar Fazli, Amr Fawzy, Nikolay Grisel Todorov, Surendra Maharjan, Wenzhao Li, Erik Linstead, Daniele Struppa, Hesham El-Askary Jun 2025

Land Instability Compounds The Risk Of Sea Level Rise In Alexandria, Egypt, Rejoice Thomas, Sara Zouriq, Shahryar Fazli, Amr Fawzy, Nikolay Grisel Todorov, Surendra Maharjan, Wenzhao Li, Erik Linstead, Daniele Struppa, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

The coastal region of Alexandria Governorate in Egypt holds significant strategic importance for trade while being susceptible to extreme weather events. It confronts a dual challenge of the rising sea levels and, as found in this study, land instability. While much attention has been rightly directed towards sea level rise (SLR), the stability of the land warrants equal consideration. Here, a comprehensive analysis of land stability is conducted in Alexandria by measuring Line of Sight (LOS) displacements and assessing their topographical, hydrological, and coastal impacts. Persistent Scatterer Interferometry technique is used to measure the LOS displacements in association with land …

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Evaluation Of Topographic Effect Parameterizations In Weather Research And Forecasting Model Over Complex Mountainous Terrain In Wildfire-Prone Regions, Yonghan Jo, Seung Hee Kim, Yungon Lee, Changki Kim, Jinkyu Hong, Junhong Lee, Keunchang Jang May 2025

Evaluation Of Topographic Effect Parameterizations In Weather Research And Forecasting Model Over Complex Mountainous Terrain In Wildfire-Prone Regions, Yonghan Jo, Seung Hee Kim, Yungon Lee, Changki Kim, Jinkyu Hong, Junhong Lee, Keunchang Jang

Institute for ECHO Articles and Research

Recent trends of intense forest fires in the Korean Peninsula have increased concerns about more extreme burning in the future under a warming climate. Accurate and reliable fire weather information has become more critical to reduce the risk of forest-related disasters over complex terrain. In this study, two parameterizations reflecting complex topographic effects were implemented in the Weather Research and Forecasting (WRF) model. The model performance was evaluated over the mountainous region in Gangwon-do, South Korea’s most significant forest area. The simulation results of the wildfire case in 2019 show that subgrid-scale orographic parameterization considerably improves model performance regarding wind …

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Escalating Hydrological Extremes And Whiplashes In The Western U.S.: Challenges For Water Management And Frontline Communities, Wenzhao Li, Surendra Maharjan, Joshua B. Fisher, Thomas Piechota, Hesham El-Askary May 2025

Escalating Hydrological Extremes And Whiplashes In The Western U.S.: Challenges For Water Management And Frontline Communities, Wenzhao Li, Surendra Maharjan, Joshua B. Fisher, Thomas Piechota, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Western U.S. is experiencing significant changes in its hydrological dynamics, marked by increased variability and rapid “whiplash” shifts between extreme drought and flood conditions. This study quantified these changes using a customized hydrological water year index, which correlated better with surface water storage in the basins than other drought/wetness indicators. Application of the index revealed heightened hydrological extremes and whiplash events post-2015 in all Western U.S. basins, with nearly 72% of stations facing critically dry conditions in 2021 and over 54% experiencing extreme wet conditions in 2023. Future projections indicate a decline of 8.5%–13.2% in non-extreme water year types …

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Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher May 2025

Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). …

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Climate Constrains The Enhancement Of Co2 Fertilization On Forest Gross Primary Productivity, Xinyuan Wei, Daniel J. Hayes, Christopher R. Schwalm, Joshua B. Fisher, Deborah N. Huntzinger, Lei Ma, Rodrigo Vargas, Nathaniel A. Brunsell May 2025

Climate Constrains The Enhancement Of Co2 Fertilization On Forest Gross Primary Productivity, Xinyuan Wei, Daniel J. Hayes, Christopher R. Schwalm, Joshua B. Fisher, Deborah N. Huntzinger, Lei Ma, Rodrigo Vargas, Nathaniel A. Brunsell

Mathematics, Physics, and Computer Science Faculty Articles and Research

Forest gross primary production (GPP) is influenced by the interplay between climate conditions and atmospheric CO2 levels, which interact in complex ways, generating both compensating and amplifying effects. In this study, eddy covariance flux measurements from 50 forest ecosystems were integrated with simulations from 14 terrestrial biosphere models to investigate how climate conditions and atmospheric CO2 concentrations regulate forest GPP. This approach bridges site-level observations with biome-scale model estimates to develop a global understanding. Our findings suggest that in boreal and cold temperate regions, temperature primarily constrains the enhancement of the CO2 fertilization on forest GPP; however, …

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Monitoring Mangrove Dynamics And Evaluating Future Afforestation Potential In The Egyptian Red Sea, Rasha M. Abou Samra, Mansour Almazroui, Wenzhao Li, Hesham El-Askary May 2025

Monitoring Mangrove Dynamics And Evaluating Future Afforestation Potential In The Egyptian Red Sea, Rasha M. Abou Samra, Mansour Almazroui, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Mangrove forests are vital for ecosystem services and coastal management but face stressors from anthropogenic activities and climate change. This study estimates the extent and afforestation potential of mangroves along the Egyptian Red Sea coast from 1984 to 2022 using NDVI derived from Landsat-5 and Sentinel-2 data in Google Earth Engine. Aboveground biomass (AGB), belowground biomass (BGB), carbon (C) stock, and CO2 sequestration potential were evaluated using Sentinel-2 and elevation data. Future afforestation suitability (2020–2050) under SSP2-4.5 and SSP5-8.5 scenarios was assessed with the MaxEnt model. Mangrove area increased from 0.95 km2 in 1984 to 1.46 km2 …

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From The Ground Up: Community-Based Participatory Research Reclaiming The Science Of Lead, Juan Manuel Rubio, Bavisha Kaylan, Anthony Diaz, Patricia Flores, Maya Cheav, David C. Bañuelas, Ashley Green, Annika Hjelmstad, Ariane Jong-Levinger, Tim Schütz, Maya Carrasquillo, Alana M. W. Lebron, Jun Wu Apr 2025

From The Ground Up: Community-Based Participatory Research Reclaiming The Science Of Lead, Juan Manuel Rubio, Bavisha Kaylan, Anthony Diaz, Patricia Flores, Maya Cheav, David C. Bañuelas, Ashley Green, Annika Hjelmstad, Ariane Jong-Levinger, Tim Schütz, Maya Carrasquillo, Alana M. W. Lebron, Jun Wu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

For decades, the dominant approach to lead poisoning has been to focus on homes affected by lead paint and to treat children who are already suffering from lead poisoning. This individualizing approach developed in the context of the defunding and deregulation of government agencies in the 1980s. In recent years, however, community–academic partnerships have reframed lead as an environmental issue produced by the development of the lead industry in the twentieth century and connected to overlapping histories of exploitation, discrimination, and inaction. These community-based projects have contributed to shifting research agendas (by emphasizing historical analysis and the study of the …

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Innovative Machine Learning, Isotopic, And Hydrogeochemical Techniques For Groundwater Analysis In Arid Landscapes In Egypt’S Eastern Desert, Saad Ahmed Mohallel, Hesham Morgan, Ali Elgendy, Surendra Maharjan, Shahryar Fazli, Wenzhao Li, Rejoice Thomas, Hesham El-Askary Apr 2025

Innovative Machine Learning, Isotopic, And Hydrogeochemical Techniques For Groundwater Analysis In Arid Landscapes In Egypt’S Eastern Desert, Saad Ahmed Mohallel, Hesham Morgan, Ali Elgendy, Surendra Maharjan, Shahryar Fazli, Wenzhao Li, Rejoice Thomas, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Groundwater serves as a lifeline in Egypt’s hyper-arid Eastern Desert, particularly for agricultural and domestic uses. However, a comprehensive understanding of groundwater origin, quality, and recharge dynamics in the region remains limited due to geological complexity, data scarcity, and the high cost of isotopic analysis. This study addresses these challenges by integrating stable isotopes (δ¹⁸O and δ²H), hydrogeochemical parameters, remote sensing, and explainable artificial intelligence (AI) to investigate groundwater dynamics and support sustainable water management strategies. A total of 34 groundwater samples were collected from three key aquifers: the Quaternary alluvium, Nubian Sandstone, and fractured Basement aquifers. Hydrochemical analyses and …

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Development And Application Of Self-Supervised Machine Learning For Smoke Plume And Active Fire Identification From The Fire Influence On Regional To Global Environments And Air Quality Datasets, Nicholas Lahaye, Anastasija Easley, Kyongsik Yun, Hugo Lee, Erik Linstead, Michael J. Garay, Olga V. Kalashnikova Apr 2025

Development And Application Of Self-Supervised Machine Learning For Smoke Plume And Active Fire Identification From The Fire Influence On Regional To Global Environments And Air Quality Datasets, Nicholas Lahaye, Anastasija Easley, Kyongsik Yun, Hugo Lee, Erik Linstead, Michael J. Garay, Olga V. Kalashnikova

Engineering Faculty Articles and Research

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% …

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Innovative Soil Classification Approach For Achieving Global Biodiversity Framework Utilizing Integrated Data Fusion Of Emit And Multispectral Satellite Observations: Case Study Of Imam Turki Bin Abdullah Royal Reserve, Kingdom Of Saudi Arabia, Hesham Morgan, Ali Elgendy, Surendra Maharjan, Wenzhao Li, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary Apr 2025

Innovative Soil Classification Approach For Achieving Global Biodiversity Framework Utilizing Integrated Data Fusion Of Emit And Multispectral Satellite Observations: Case Study Of Imam Turki Bin Abdullah Royal Reserve, Kingdom Of Saudi Arabia, Hesham Morgan, Ali Elgendy, Surendra Maharjan, Wenzhao Li, 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

Soil classification is essential for sustainable land management, ecological conservation, and combating desertification, particularly in arid and semi-arid regions. This study integrates hyperspectral data from the Earth Surface Mineral Dust Source Investigation (EMIT) and multispectral imagery from Sentinel-2 to achieve accurate soil classification for the Imam Turki bin Abdullah Royal Reserve (ITBA) in Saudi Arabia. Using advanced Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), the study highlights the power of data fusion in addressing the limitations of standalone remote sensing methods. The integration of hyperspectral and multispectral data combines the spectral richness of hyperspectral imaging with the spatial …

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Transfer Learning In Junction With A Light Use Efficiency Model For Estimating Grassland Gross Primary Production, Ruiyang Yu, Yunjun Yao, Qingxin Tang, Xueyi Zhang, Changliang Shao, Joshua B. Fisher, Jiquan Chen, Xiaotong Zhang, Yufu Li, Jia Xu, Lu Liu, Zijing Xie, Jing Ning, Jiahui Fan, Luna Zhang Mar 2025

Transfer Learning In Junction With A Light Use Efficiency Model For Estimating Grassland Gross Primary Production, Ruiyang Yu, Yunjun Yao, Qingxin Tang, Xueyi Zhang, Changliang Shao, Joshua B. Fisher, Jiquan Chen, Xiaotong Zhang, Yufu Li, Jia Xu, Lu Liu, Zijing Xie, Jing Ning, Jiahui Fan, Luna Zhang

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

It is significant to simulate grassland gross primary production (GPP) to understand the terrestrial carbon budget over Inner Mongolia (IMG), China. Nevertheless, there is not sufficient in situ GPP data over this region. In this study, we proposed a novel model-based transfer learning (MTL) approach with generative adversarial networks-long short-term memory (GAN-LSTM) and light use efficiency (LUE) models to derive grassland GPP over IMG, China. We first used 25 grassland eddy covariance sites over the conterminous United States to establish the GAN-LSTM model and then fine-tuned it with six sites over IMG to estimate water constraints that were embedded into …

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Predicting Precipitation And Ndvi Utilization Of The Multi-Level Linear Mixed-Effects Model And The Ca-Markov Simulation Model, Fatima Belhaj, Hlila Rachid, Ouallali Abdessalam, Aqil Tariq, Belkendil Abdeldjalil, Beroho Mohamed, Hassan Alzahrani, Hajra Mustafa, Hesham El-Askary Mar 2025

Predicting Precipitation And Ndvi Utilization Of The Multi-Level Linear Mixed-Effects Model And The Ca-Markov Simulation Model, Fatima Belhaj, Hlila Rachid, Ouallali Abdessalam, Aqil Tariq, Belkendil Abdeldjalil, Beroho Mohamed, Hassan Alzahrani, Hajra Mustafa, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

The current work intends to reconstruct the spatiotemporal evolution of precipitation and the Normalized Differentiate Vegetation Index (NDVI) in the Loukkos watershed and provide scenarios for their recent and future evolution, therefore determining the degree of association. We conducted a study on the time series data of precipitation and NDVI from 1999 to 2019. The NDVI prediction is conducted using the CA-Markov model and the linear mixed-effects multi-level model (LME) with precipitation data from 2019 to 2040. The CA-Markov model was employed to predict the vegetation indices for 2029 and 2040 using 1999, 2009, and 2019 data. The model simulates …

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Reviews And Syntheses: Variable Inundation Across Earth’S Terrestrial Ecosystems, James Stegen, Amy J. Burgin, Michelle H. Busch, Joshua B. Fisher, Joshua Ladau, Jenna Abrahamson, Lauren Kinsman-Costello, Li Li, Xingyuan Chen, Thibault Datry, Nate Mcdowell, Corianne Tatariw, Anna Braswell, Jillian M. Deines, Julia A. Guimond, Peter Regler, Kenton Rod, Edward K. P. Bam, Etienne Fluet-Chouinard, Inke Forbrich, Kristin L. Jaeger, Teri O'Meara, Tim Scheibe, Erin Seybold, Jon N. Sweetman, Jianqiu Zheng, Daniel C. Allen, Elizabeth Herndon, Beth A. Middleton, Scott Painter, Kevin Roche, Julianne Scamardo, Ross Vander Vorste, Kristin Boye, Ellen Wohl, Margaret Zimmer, Kelly Hondula, Maggi Laan, Anna Marshall, Kaizad F. Patel Feb 2025

Reviews And Syntheses: Variable Inundation Across Earth’S Terrestrial Ecosystems, James Stegen, Amy J. Burgin, Michelle H. Busch, Joshua B. Fisher, Joshua Ladau, Jenna Abrahamson, Lauren Kinsman-Costello, Li Li, Xingyuan Chen, Thibault Datry, Nate Mcdowell, Corianne Tatariw, Anna Braswell, Jillian M. Deines, Julia A. Guimond, Peter Regler, Kenton Rod, Edward K. P. Bam, Etienne Fluet-Chouinard, Inke Forbrich, Kristin L. Jaeger, Teri O'Meara, Tim Scheibe, Erin Seybold, Jon N. Sweetman, Jianqiu Zheng, Daniel C. Allen, Elizabeth Herndon, Beth A. Middleton, Scott Painter, Kevin Roche, Julianne Scamardo, Ross Vander Vorste, Kristin Boye, Ellen Wohl, Margaret Zimmer, Kelly Hondula, Maggi Laan, Anna Marshall, Kaizad F. Patel

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The structure, function, and dynamics of Earth's terrestrial ecosystems are profoundly influenced by how often (frequency) and how long (duration) they are inundated with water. A diverse array of natural and human-engineered systems experience temporally variable inundation whereby they fluctuate between inundated and non-inundated states. Variable inundation spans extreme events to predictable sub-daily cycles. Variably inundated ecosystems (VIEs) include hillslopes, non-perennial streams, wetlands, floodplains, temporary ponds, tidal systems, storm-impacted coastal zones, and human-engineered systems. VIEs are diverse in terms of inundation regimes, water chemistry and flow velocity, soil and sediment properties, vegetation, and many other properties. The spatial and temporal …

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Landslide Susceptibility Assessment Of The Wanzhou District: Merging Landslide Susceptibility Modelling (Lsm) With Insar-Derived Ground Deformation Map, Chao Zhou, Lulu Gan, Ying Cao, Yue Wang, Samuele Segoni, Xuguo Shi, Mahdi Motagh, Ramesh P. Singh Jan 2025

Landslide Susceptibility Assessment Of The Wanzhou District: Merging Landslide Susceptibility Modelling (Lsm) With Insar-Derived Ground Deformation Map, Chao Zhou, Lulu Gan, Ying Cao, Yue Wang, Samuele Segoni, Xuguo Shi, Mahdi Motagh, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility maps. Here, to address this problem, we proposed a method to produce more accurate and dynamic landslide susceptibility maps by coupling advanced Ensemble Machine Learning (EML) and Multi-Temporal Interferometric SAR (MT-InSAR). The Wanzhou District in Three Gorges Reservoir area of China is considered as the test site. The …

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