Environmental Monitoring Commons^™

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% …

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 …

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 …

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 …

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 …

Glacial Lakes Outburst Susceptibility And Risk In The Eastern Himalayas Using Analytical Hierarchy Process And Backpropagation Neural Network Models, Sandeep Kumar Mondal, Jyotindra Narayan, Chitesh Sharma, Rishikesh Bharti, Santosha Kumar Dwivedy, Pinaki Roy Chowdhury

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Himalayan cryosphere is dynamic, and changing climate conditions threaten breach of glacial lakes. A number of glacial lake outburst floods (GLOFs) occurred in the Himalayas in the recent past, affecting people and infrastructures. Assessment of high-altitude glacial lakes is required to avoid associated hazards and mitigate the impacts. In this study, we have made an inventory of naturally formed lakes within the Sikkim Himalayas, including Nepal, Bhutan, and China, and discussed the GLOF susceptibility. A total of 399 lakes have been identified, out of which 281 lakes have an areal coverage greater than 0.01 Km2. Monitoring temporal changes shows …

Multi-Sensor Data Fusion And Gis-Drastic Integration For Groundwater Vulnerability Assessment With Rainfall Consideration, Wu Jiazhe, Dai Xinrui, Su Yangcheng, Zheng Xiangtian, Bushra Ghaffar, Rabiya Nasir, Ahsan Jamil, Zeeshan Zafar, Mohammad Suhail Meer, M. Abdullah-Al-Wadud, Rahila Naseer, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

In many areas of the world, particularly in arid and semi-arid regions, groundwater is the primary source of fresh water, and it supplies around one-third of the world's fresh water. Agriculture is the primary economic sector on the coast in the southern district (Nowshera). More food productivity is required due to the expanding population and diminishing agricultural lands, which increases the use of chemical pesticides and fertilizers in farming. The current study was conducted in northwestern parts of Pakistan to evaluate the impacts of the frequent use of pesticides and fertilizers in agricultural fields. Nine hydrogeological parameters were considered, and …

Unfolding Cascading Impacts Of Changing South Asia Monsoon On A Hindu Kush Himalayas Basin, Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Arshad Ansari, Suraj Tiwari, Roma Thakurathi, Rejoice Thomas, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Study region

Study focus

New hydrological insights for the region

Extraordinary 21st Century Drought In The Po River Basin (Italy), Abel Andrés Ramírez Molina, Glenn Tootle, Giuseppe Formetta, Thomas Piechota, Jiaqi Gong

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Recent research identified 2022 as being the year of lowest seasonal April–May–June–July (AMJJ) observed streamflow for the Po River Basin (PRB) in the past two centuries. Expanding upon this research, we applied filters (2-year to 30-year filters) to the AMJJ observed streamflow and identified the late 20th and 21st century as displaying extreme drought. In this study, we introduce PALEO-RECON, an automated reconstruction tool developed to leverage tree ring-based proxies and streamline regression processes. Using PALEO-RECON, we reconstructed the AMJJ streamflow, applying traditional regression techniques and using a nested approach in which 30-, 40-, and 50-year windows within the ~200-year …

Invasion Stress Mitigates Climate Stress In A Brackish Marsh Amphipod, Lorna E. Haworth, Sarah Nancollas, Susie N. Landa, Anne E. Todgham, Richelle L. Tanner

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

1. Organisms in coastal brackish ecosystems face not only highly variable environmental conditions, but the intensity and stochasticity of these environmental conditions are anticipated to increase due to climate change. While environmental changes are often presented unilaterally as stressors, there are some anthropogenic environmental transformations, such as the introduction of habitat forming plant species, that may mitigate physiological stress imposed by warming. In Suisun Marsh in the California Delta, native plant canopies allow light and heat to penetrate to the understory yet the canopies of the introduced Phragmites australis reed block out sunlight and heat for organisms living below.
2. We set …

Identifying Opportunities For Nature-Based Solutions With Geospatialized Life Cycle Assessments And Fine-Scale Socioecological Data, Gabriela Shirkey, Annick Anctil, Ranjeet John, Venkatesh Kolluru, Leah Mungai, Herve Kashongwe, Lauren T. Cooper, Ilke Celik, Joshua B. Fisher, Jiquan Chen

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

As we increasingly understand the impact that land management intensification has on local and global climate, the call for nature-based solutions (NbS) in agroecosystems has expanded. Moreover, the pressing need to determine when and where NbS should be used raises challenges to socioecological data integration as we overcome spatiotemporal resolutions. Natural and working lands is an effort promoting NbS, particularly emissions reduction and carbon stock maintenance in forests. To overcome the spatiotemporal limitation, we integrated life cycle assessments (LCA), an ecological carbon stock model, and a land cover land use change model to synthesize rates of global warming potential (GWP) …

Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, …

Multi-Temporal Analysis Of Urbanization-Driven Slope And Ecological Impact Using Machine-Learning And Remote Sensing Techniques, Zhang Hao, Muhammad Haseeb, Zheng Xiangtian, Zainab Tahir, Syed Amer Mahmood, Aqil Tariq, Rana Waqar Aslam, M. Abdullah-Al-Wadud, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Rapid urbanization in Lahore, Pakistan, has led to significant ecological and thermal challenges, particularly the intensification of Urban Heat Island (UHI) effects and increased thermal stress as measured by the Urban Thermal Field Variance Index (UTFVI). This study employs a multi-temporal evaluation of Landsat satellite imagery and GIS-based analysis to investigate the Spatio-temporal trends in land-use and land-cover (LULC) changes from 1994 to 2024. We detected substantial changes in urban growth, vegetation cover, and barren areas using supervised classification (Random Forest) methods and remote sensing indices such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), NDBI (Normalized …

Spatial Gap-Filling Of Himawari-8 Hourly Aod Products Using Machine Learning With Model-Based Aod And Meteorological Data: A Focus On The Korean Peninsula, Youjeong Youn, Seoyeon Kim, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, …

Changing Characteristics Of Land Cover, Landscape Pattern And Ecosystem Services In The Bohai Rim Region Of China, Jiaqi Liu, Wei Chen, Hu Ding, Zhanhang Liu, Min Xu, Ramesh P. Singh, Congqiang Liu

Mathematics, Physics, and Computer Science Faculty Articles and Research

Since the Anthropocene, ecosystems have been continuously deteriorating due to global climate change and human intervention. Exploring the changing characteristics of land use/land cover (LULC), landscape pattern and ecosystem service (ES) and their drivers is crucial for regional ecosystem management and sustainable development. Taking the Bohai Rim region of China as an example, we used the land use transfer matrix, landscape pattern index and InVEST model to analyze the changing characteristics of LULC, landscape pattern and six key ESs [crop production (CP), water yield (WY), carbon storage (CS), soil conservation (SC), habitat quality (HQ), landscape aesthetics (LA)] during 2000–2020. Detailed …

Coupling Between Evapotranspiration, Water Use Efficiency, And Evaporative Stress Index Strengthens After Wildfires In New Mexico, Usa, Ryan C. Joshi, Annalise Jensen, Madeleine Pascolini-Campbell, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Aim

Methods

Results …

Examining Wildfire Dynamics Using Ecostress Data With Machine Learning Approaches: The Case Of South-Eastern Australia's Black Summer, Yuanhui Zhu, Shakthi B. Murugesan, Ivone K. Masara, Soe W. Myint, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wildfires are increasing in risk and prevalence. The most destructive wildfires in decades in Australia occurred in 2019–2020. However, there is still a challenge in developing effective models to understand the likelihood of wildfire spread (susceptibility) and pre-fire vegetation conditions. The recent launch of NASA's ECOSTRESS presents an opportunity to monitor fire dynamics with a high resolution of 70 m by measuring ecosystem stress and drought conditions preceding wildfires. We incorporated ECOSTRESS data, vegetation indices, rainfall, and topographic data as independent variables and fire events as dependent variables into machine learning algorithms applied to the historic Australian wildfires of 2019–2020. …

Carbon Restoration Potential On Global Land Under Water Resource Constraints, Shouzhang Peng, César Terrer, Benjamin Smith, Philippe Ciais, Qinggong Han, Jialan Nan, Joshua B. Fisher, Liang Chen, Lei Deng, Kailiang Yu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Ecosystem restoration is a critical nature-based solution to mitigate climate change. However, the carbon sequestration potential of restoration, defined as the maximum achievable carbon storage, has likely been overestimated because previous studies have not adequately accounted for the competition between ecosystem water demands for maximizing carbon sequestration and human water needs. Here we used a comprehensive process-based model combined with extensive land-use data and evaporation recycling accounting for land–atmosphere feedback to estimate the water requirements associated with ecosystem restoration. We found that achieving the carbon sequestration potential of restoration would significantly reduce global water availability per capita by 26%, posing …

Scaling Individual Tree Transpiration With Thermal Cameras Reveals Interspecies Differences To Drought Vulnerability, Mostafa Javadian, Donald M. Aubrecht, Joshua B. Fisher, Russell L. Scott, Sean P. Burns, Jen L. Diehl, J. William Munger, Andrew D. Richardson

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Understanding tree transpiration variability is vital for assessing ecosystem water-use efficiency and forest health amid climate change, yet most landscape-level measurements do not differentiate individual trees. Using canopy temperature data from thermal cameras, we estimated the transpiration rates of individual trees at Harvard Forest and Niwot Ridge. PT-JPL model was used to derive latent heat flux from thermal images at the canopy-level, showing strong agreement with tower measurements (R2 = 0.70–0.96 at Niwot, 0.59–0.78 at Harvard at half-hourly to monthly scales) and daily RMSE of 33.5 W/m2 (Niwot) and 52.8 W/m2 (Harvard). Tree-level analysis revealed species-specific responses to …

Challenges And Future Directions In Quantifying Terrestrial Evapotranspiration, Koong Yi, Gabriel B. Senay, Joshua B. Fisher, Lixin Wang, Kosana Suvočarev, Housen Chu, Georgianne W. Moore, Kimberly A. Novick, Mallory L. Barnes, Trevor F. Keenan, Kanishka Mallick, Xiangzhong Luo, Justine E. C. Missik, Kyle B. Delwiche, Jacob A. Nelson, Stephen P. Good, Xiangming Xiao, Steven A. Kannenberg, Arman Ahmadi, Tianxin Wang, Gil Bohrer, Marcy E. Litvak, David E. Reed, A. Christopher Oishi, Margaret S. Torn, Dennis Baldocchi

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Terrestrial evapotranspiration is the second-largest component of the land water cycle, linking the water, energy, and carbon cycles and influencing the productivity and health of ecosystems. The dynamics of ET across a spectrum of spatiotemporal scales and their controls remain an active focus of research across different science disciplines. Here, we provide an overview of the current state of ET science across in situ measurements, partitioning of ET, and remote sensing, and discuss how different approaches complement one another based on their advantages and shortcomings. We aim to facilitate collaboration among a cross-disciplinary group of ET scientists to overcome the …

Detection Of Seismic Microwave Radiation Anomalies In Snow-Covered Mountainous Terrain: Insights From Two Recent Earthquakes In The Pamir–Tien Shan Region, Feng Jing, Meng Jiang, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

When earthquakes occur in high-mountain areas during the winter season, the epicentral region is often covered by a snow layer, which can be either thin or thick. The presence of snow and/or ice layers affects the detection of thermal anomalies associated with seismic signals. Taking into account the penetration capabilities of microwaves, microwave brightness temperature data were analyzed by using the index of microwave radiation anomaly to study the response of the epicentral region associated with two recent strong earthquakes in Central Asia, which occurred in snow-covered mountainous areas. Increased microwave radiation was observed within one week prior to the …

Satellite Observations Indicate That Chia Uses Less Water Than Other Crops In Warm Climates, Brian Kirsch, Joshua B. Fisher, Thomas Piechota, Mohammad Hassani, Diego C. Suardiaz, Radhika Puri, Joseph Cahill, Hagop S. Atamian

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Many parts of the world face severe and prolonged drought conditions, stressing the sustainability of water resources and agriculture. Transitioning to water-efficient crops is one strategy that can help adapt to water scarcity. An emerging drought-tolerant crop of interest is chia (Salvia hispanica). Yet, no study has compared its large-scale water use dynamics to those of widely established crops across the globe. Here, we use satellite data over multiple years to assess the water use efficiency of chia, alfalfa, corn, and soybean globally. Results show that chia consumed 13-38% less water than alfalfa, corn, and soy and assimilated …

Seasonal Dynamics In Land Surface Temperature In Response To Land Use Land Cover Changes Using Google Earth Engine, Lei Feng, Sajjad Hussain, Narcisa G. Pricope, Sana Arshad, Aqil Tariq, Li Feng, Muhammad Mubeen, Rana Waqar Aslam, Mohammed S. Fnais, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Changes in land use and land cover (LULC) are critical for evaluating global spatiotemporal trends, especially regarding climate change and urbanization. This study investigates the dynamics of Landsat surface temperature (LST) in response to LULC changes and their effects on the seasonal microclimate in Kasur District, Pakistan. Using the Google Earth Engine platform, we employed a random forest algorithm to detect LULC changes (cropland, forest, built-up, fallow, barren, and water) and analyze seasonal spectral indices from Landsat imagery for 1988, 2002, and 2022. Significant LULC changes were observed, including a 9.8% increase in built-up areas, a 4.2% decrease in cropland, …

A Generalized Machine Learning Model For Long-Term Coral Reef Monitoring In The Red Sea, Justin J. Gapper, Surendra Maharjan, Wenzhao Li, Erik Linstead, Surya Prakash Tiwari, Mohamed A. Qurban, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground …

An Automated Machine Learning Approach To The Retrieval Of Daily Soil Moisture In South Korea Using Satellite Images, Meteorological Data, And Digital Elevation Model, Nari Kim, Soo-Jin Lee, Eunha Sohn, Mija Kim, Seonkyeong Seong, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence …

Methane Fluxes In Tidal Marshes Of The Conterminous United States, Ariane Arias-Ortiz, Jaxine Wolfe, Scott D. Bridgham, Sara Knox, Gavin Mcnicol, Brian A. Needelman, Julie Shahan, Ellen J. Stuart-Haëntjens, Lisamarie Windham-Myers, Patty Y. Oikawa, Dennis D. Baldocchi, Joshua S. Caplan, Margaret Capooci, Kenneth M. Czapla, R. Kyle Derby, Heida L. Diefenderfer, Inke Forbrich, Gina Groseclose, Jason K. Keller, Cheryl Kelley, Amir E. Keshta, Helena S. Kleiner, Ken W. Krauss, Robert R. Lane, Sarah Mack, Serena Moseman-Valtierra, Thomas J. Mozdzer, Peter Mueller, Scott C. Neubauer, Genevieve Noyce, Katrina V. R. Schäfer, Rebecca Sanders-Demott, Charles A. Schutte, Rodrigo Vargas, Nathaniel B. Weston, Benjamin Wilson, J. Patrick Megonigal, James R. Homquist

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Methane (CH4) is a potent greenhouse gas (GHG) with atmospheric concentrations that have nearly tripled since pre-industrial times. Wetlands account for a large share of global CH4 emissions, yet the magnitude and factors controlling CH4 fluxes in tidal wetlands remain uncertain. We synthesized CH4 flux data from 100 chamber and 9 eddy covariance (EC) sites across tidal marshes in the conterminous United States to assess controlling factors and improve predictions of CH4 emissions. This effort included creating an open-source database of chamber-based GHG fluxes (https://doi.org/10.25573/serc.14227085). Annual fluxes across chamber and EC sites averaged 26 ± 53 g CH4 …

Spatial Optimism And Cross-Over Effects In The Perceptions Of Interconnected Wildfire, Flood, And Mudslide Hazards, Douglas Houston, Omar Pérez Figueroa, Ariane Jong-Levinger, Brett F. Sanders

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Communities near the wildland urban interface (WUI) are exposed to a mix of three interconnected hazards (wildfire, flood, and mudslide), and understanding multi-hazard perceptions is critically important for emergency preparation and hazard mitigation—particularly given the WUI’s rapid expansion and intensifying environmental hazards. Based on a survey of residents living near recent burn scars in Southern California, we document cross-over effects in hazard perceptions, where resident experience with one hazard was associated with greater hazard rankings for other hazards. Additionally, for all three hazards analyzed we document perceptions of increasing hazard levels with increasing spatial scales (home, near-home, neighborhood, and community), …

Estimating Post-Fire Flood Infrastructure Clogging And Overtopping Hazards, Ariane Jong-Levinger, Douglas Houston, Brett F. Sanders

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Cycles of wildfire and rainfall produce sediment-laden floods that pose a hazard to development and may clog or overtop protective infrastructure, including debris basins and flood channels. The compound, post-fire flood hazards associated with infrastructure overtopping and clogging are challenging to estimate due to the need to account for interactions between sequences of wildfire and storm events and their impact on flood control infrastructure over time. Here we present data sources and calibration methods to estimate infrastructure clogging and channel overtopping hazards on a catchment-by-catchment basis using the Post-Fire Flood Hazard Model (PF2HazMo), a stochastic modeling approach that utilizes continuous …

Impact Of Land Use Dynamics On The Water Yields In The Gorgan River Basin, Masoomeh Yaghoobi, Aram Fathi, Shahryar Fazli, Wenzhao Li, Elham Haghshenas, Vahid Shokri Kuchak, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

This research investigates the future dynamics of water yield services in the Gorgan River Basin in the North of Iran by analyzing land cover changes from 1990 to 2020, using Landsat images and predicting up to 2040 with the Land Change Modeler and InVEST model under three scenarios: continuation, conservation, and mitigation. The results indicate significant shifts in agricultural land impacted water yields, which fluctuated from 324.7 million cubic meters (MCM) in 1990 to 279.7 MCM in 2010, before rising to 320.1 MCM by 2020. The study uniquely assesses the effects of land use changes on water yields, projecting a …

A Comparative Analysis Of Openet For Evaluating Evapotranspiration In California Almond Orchards, Kyle Knipper, Martha Anderson, Nicholas Bambach, Forrest Melton, Zac Ellis, Yun Yang, John Volk, Andrew J. Mcelrone, William Kustas, Matthew Roby, Will Carrara, Sebastian Castro, Ayse Kilic, Joshua B. Fisher, Anderson Ruhoff, Gabriel B. Senay, Charles Morton, Sebastian Saa, Richard G. Allen

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in …