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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Variation in forest root image annotation by experts, novices, and AI

Abstract Background The manual study of root dynamics using images requires huge investments of time and resources and is prone to previously poorly quantified annotator bias. Artificial intelligence (AI) image-processing tools have been successful in overcoming limitations of manual annotation in homogeneous soils, but their efficiency and accuracy is yet to be widely tested on less homogenous, non-agricultural soil profiles, e.g., that of forests, from which data on root dynamics are key to understanding the carbon cycle. Here, we quantify variance in root length measured by human annotators with varying experience levels. We evaluate the application of a convolutional neural network (CNN) model, trained on a software accessible to researchers without a machine learning background, on a heterogeneous minirhizotron image dataset taken in a multispecies, mature, deciduous temperate forest. Results Less experienced annotators consistently identified more root length than experienced annotators. Root length annotation also varied between experienced annotators. The CNN root length results were neither precise nor accurate, taking ~ 10% of the time but significantly overestimating root length compared to expert manual annotation ( p = 0.01). The CNN net root length change results were closer to manual ( p = 0.08) but there remained substantial variation. Conclusions Manual root length annotation is contingent on the individual annotator. The only accessible CNN model cannot yet produce root data of sufficient accuracy and precision for ecological applications when applied to a complex, heterogeneous forest image dataset. A continuing evaluation and development of accessible CNNs for natural ecosystems is required.

Handy, Grace↗
NASA NTRSDate not supplied

Principal Component and Machine Learning Approach to Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals can be limited spatially due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals. Despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we developed a spatial gap filling approach applying machine learning approach to hyperspectral instruments to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral components that describe the scattering and absorption of the atmosphere mixed with the surface spectral signatures. The coefficients of the principal components are used to train a neural network to predict ocean color properties derived from a standard MODIS ocean color algorithm. We apply the approach to two hyperspectral UV/VIS sensors, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) onboard upcoming NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the first NASA and Smithsonian geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) spectrometer to better understand diurnal variability in inland and coastal ocean ecology.

Zachary Fasnacht↗
NASA NTRSDate not supplied

Use of TEMPO as a Proxy for Hyperspectral Geostationary Ocean Color Measurements from the GeoXO OCX Instrument: Harnessing Machine Learning and Principal Component Techniques for Atmospheric and Glint Correction

Retrievals of ocean color from space are important for better understanding the ocean ecosystem. The launch of atmospheric geostationary hyperspectral sensors such as TEMPO, provides a unique opportunity to examine the diurnal variability in ocean ecology. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO Ocean Color instrument (OCX), its hourly measurements provide coverage of regions such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. These data can be useful for testing new algorithms. We will apply our newly developed machine learning based atmospheric correction approach for ocean color retrievals to TEMPO data. Our approach begins by decomposing measured radiances from hyperspectral sensors into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from collocated MODIS/VIIRS physically-based retrievals. This machine learning approach does not rely on radiative transfer modeling, and the use of MODIS/VIIRS data for training accounts for possible calibration b in hyperspectral data. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate ocean color properties in less-than-ideal conditions such as lightly to moderately clouded conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it will provide collocated measurements at green and red wavelengths that were not available from OMI and TROPOMI and are important particularly for coastal waters. Additionally, our technique can be applied early in the mission and has potential to demonstrate the value of near real time ocean color products that are important for monitoring of harmful algae blooms and other oceanic phenomena.

Zachary Fasnacht↗
DOE OSTI2025

An approach to urban waterway assessment using holistic values and reciprocity

Current aquatic ecosystem assessment methods and tools often focus on physical, chemical, and biological indicators of ecosystem health. This approach to ecosystem assessment is not always straightforward to execute in urban environments and ignores potential connectivity between social and environmental outcomes. During a workshop at the Symposium on Urbanization and Stream Ecology in Brisbane, Australia in 2023 (SUSE6), we developed an approach to urban aquatic ecosystem assessment that incorporates a holistic perspective. Specifically, our approach considers both environmental (biological, chemical, and physical integrity) and social (community connection, human safety, resource use) values of urban waterways. This approach is inclusive of Indigenous perspectives, such as the concept of reciprocity, whereby consideration of both the environment and society leads to a healthy ecosystem. Here, to highlight how this holistic assessment approach could be used, we present real-world examples that included assessing both environmental and societal values informed by reciprocity or balanced perspectives. This approach can be broadly applied and adapted to specific aquatic ecosystem conditions and projects, providing an inclusive, community-centered approach for assessing the health of waterways in urban environments.

Indigenous knowledge↗
NASA NTRS2021

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods toforecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

Machine Learning↗
NASA NTRSDate not supplied

Expanding the Spatiotemporal Range of Soil Moisture Analysis Utilizing NASA Earth Observations and In Situ Measurements

Drought can cause immense agricultural and ecological damage resulting in high mitigation and compensation costs. Climate variability in future decades is expected to cause severe drought conditions and threaten necessary water resources. Stakeholders seek to implement effective drought assessments in preparation for potential economic and environmental damage invoked by drought. Although in-situ measurements are accurate, the current infrastructure is spatially limited and costly to maintain. A framework was created to compare modeled, satellite and in-situ data in drought monitoring. Here we show that the comparison of in-situ and remotely sensed soil moisture (SM) measurements can increase the spatiotemporal range of SM assessments. Data collected between 2003 and 2021 by NASA’s SPoRT Land Information System (SPoRT-LIS) and Soil Moisture Active Passive (SMAP) mission were standardized and compared with in-situ data provided through the Illinois Climate Network (WARM). Statistical analysis results including the Pearson correlation coefficient (r), root mean squared error, mean absolute error and others were calculated to compare the WARM measurements to the SMAP and SPoRT-LIS products. Results indicate that both satellite products demonstrate seasonally variable bias that is not present in the in-situ measurements. Bias was highest in the winter months and lowest in the late summer and early fall months in both satellite datasets. Overall, WARM-SPoRT comparisons resulted in lower seasonal variability. However, on average, the SMAP comparison demonstrated higher correlation values and lower error values. The WARM-SMAP average correlation (r) was 0.61 compared to the WARM-SPoRT average correlation (r) value of 0.54. Average mean absolute error values calculated for the SMAP and SPoRT comparisons were 0.07 and 0.08 percent soil moisture by volume, respectively. These analyses suggest integrating in-situ measurements and those provided by NASA Earth observations can be utilized in a multi-faceted SM evaluation, a valuable contribution to drought monitoring and water resource decision making.

Emma P Myrick↗
DOE OSTI2026

Machine learning of factors for improving oyster hatchery production

Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis offered crucial insights into model interpretations, highlighting week number, Normalized Difference Vegetation Index, salinity, turbidity, and fecundity as primary drivers of yield variability. For low-yield cases, salinity-related variables were particularly important. Our findings provide an early warning system for potential production downturns, empowering hatchery operators to make data-driven decisions for optimizing water conditions, feeding schedules, and broodstock management. By boosting predictability and efficiency, this research directly supports economic stability of the oyster industry and ecological health of the Chesapeake Bay.

Vishwakarma, Srishti [Oak Ridge National Laborator↗
DOE OSTI2024

Plan evaluation for heat resilience: complementary methods to comprehensively assess heat planning in Tempe and Tucson, Arizona

Abstract Escalating impacts from climate change and urban heat are increasing the urgency for communities to equitably plan for heat resilience. Cities in the desert Southwest are among the hottest and fastest warming in the U.S., placing them on the front lines of heat planning. Urban heat resilience requires an integrated planning approach that coordinates strategies across the network of plans that shape the built environment and risk patterns. To date, few studies have assessed cities’ progress on heat planning. This research is the first to combine two emerging plan evaluation approaches to examine how networks of plans shape urban heat resilience through case studies of Tempe and Tucson, Arizona. The first methodology, Plan Quality Evaluation for Heat Resilience, adapts existing plan quality assessment approaches to heat. We assess whether plans meet 56 criteria across seven principles of high-quality planning and the types of heat strategies included in the plans. The second methodology, the Plan Integration for Resilience Scorecard™ (PIRS™) for Heat, focuses on plan policies that could influence urban heat hazards. We categorize policies by policy tool and heat mitigation strategy and score them based on their heat impact. Scored policies are then mapped to evaluate their spatial distribution and the net effect of the plan network. The resulting PIRS™ for Heat scorecard is compared with heat vulnerability indicators to assess policy alignment with risks. We find that both cities are proactively planning for heat resilience using similar plan and strategy types, however, there are clear and consistent opportunities for improvement. Combining these complementary plan evaluation methods provides a more comprehensive understanding of how plans address heat and a generalizable approach that communities everywhere could use to identify opportunities for improved heat resilience planning.

Environmental Sciences & Ecology↗
NASA NTRSDate not supplied

A Principal Component and Machine Learning Approach to Spatially Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals tend to be limited in spatial coverage due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals but despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we propose a spatial gap filling approach using machine learning to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from a standard ocean color algorithm such as the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) which will be onboard NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the geostationary satellite Tropospheric Emissions: Monitoring of Pollution (TEMPO) to better understand diurnal variability in ocean ecology.

MODIS atmospheric correction algorithm↗
NASA NTRS2010

Land Surface Phenology from MODIS: Characterization of the Collection 5 Global Land Cover Dynamics Product

Information related to land surface phenology is important for a variety of applications. For example, phenology is widely used as a diagnostic of ecosystem response to global change. In addition, phenology influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. Increasingly, the importance of phenology for studies of habitat and biodiversity is also being recognized. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, remote sensing provides the only way to observe and monitor phenology over large scales and at regular intervals. The MODIS Global Land Cover Dynamics Product was developed to support investigations that require regional to global scale information related to spatiotemporal dynamics in land surface phenology. Here we describe the Collection 5 version of this product, which represents a substantial refinement relative to the Collection 4 product. This new version provides information related to land surface phenology at higher spatial resolution than Collection 4 (500-m vs. 1-km), and is based on 8-day instead of 16-day input data. The paper presents a brief overview of the algorithm, followed by an assessment of the product. To this end, we present (1) a comparison of results from Collection 5 versus Collection 4 for selected MODIS tiles that span a range of climate and ecological conditions, (2) a characterization of interannual variation in Collections 4 and 5 data for North America from 2001 to 2006, and (3) a comparison of Collection 5 results against ground observations for two forest sites in the northeastern United States. Results show that the Collection 5 product is qualitatively similar to Collection 4. However, Collection 5 has fewer missing values outside of regions with persistent cloud cover and atmospheric aerosols. Interannual variability in Collection 5 is consistent with expected ranges of variance suggesting that the algorithm is reliable and robust, except in the tropics where some systematic differences are observed. Finally, comparisons with ground data suggest that the algorithm is performing well, but that end of season metrics associated with vegetation senescence and dormancy have higher uncertainties than start of season metrics.

Land cover dynamics↗
DOE OSTI2024

Genomic fingerprints of the world’s soil ecosystems

Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1,512 manually curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes. Our objective is to uncover novel biogeographical patterns of soil microbiomes across environmental factors and ecological biomes with high molecular resolution. We reveal shifts in the potential for (i) microbial nutrient acquisition across pH gradients; (ii) stress-, transport-, and redox-based processes across changes in soil bulk density; and (iii) greenhouse gas emissions across biomes. We also use an unsupervised approach to reveal a collection of soils with distinct genomic signatures, characterized by coordinated changes in soil organic carbon, nitrogen, and cation exchange capacity and in bulk density and clay content that may ultimately reflect soil environments with high microbial activity. Genomic fingerprints for these soils highlight the importance of resource scavenging, plant-microbe interactions, fungi, and heterotrophic metabolisms. Across all analyses, we observed phylogenetic coherence in soil microbiomes—more closely related microorganisms tended to move congruently in response to soil factors. Collectively, the genomic fingerprints uncovered here present a basis for global patterns in the microbial mechanisms underlying soil biogeochemistry and help beget tractable microbial reaction networks for incorporation into process-based models of soil carbon and nutrient cycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbiology↗
NASA NTRS2007

Solutions Network Formulation Report. Landsat Data Continuity Mission Simulated Data Products for Bureau of Land Management and Environmental Protection Agency Abandoned Mine Lands Decision Support

Presently, the BLM (Bureau of Land Management) has identified a multitude of abandoned mine sites in primarily Western states for cleanup. These sites are prioritized and appropriate cleanup has been called in to reclaim the sites. The task is great in needing considerable amounts of agency resources. For instance, in Colorado alone there exists an estimated 23,000 abandoned mines. The problem is not limited to Colorado or to the United States. Cooperation for reclamation is sought at local, state, and federal agency level to aid in identification, inventory, and cleanup efforts. Dangers posed by abandoned mines are recognized widely and will tend to increase with time because some of these areas are increasingly used for recreation and, in some cases, have been or are in the process of development. In some cases, mines are often vandalized once they are closed. The perpetrators leave them open, so others can then access the mines without realizing the danger posed. Abandoned mine workings often fill with water or oxygen-deficient air and dangerous gases following mining. If the workings are accidentally entered into, water or bad air can prove fatal to those underground. Moreover, mine residue drainage negatively impacts the local watershed ecology. Some of the major hazards that might be monitored by higher-resolution satellites include acid mine drainage, clogged streams, impoundments, slides, piles, embankments, hazardous equipment or facilities, surface burning, smoke from underground fires, and mine openings.

Estep, Leland↗
DOE OSTI2025

Three-dimensional modeling of hyphal fusion, branching, and nutrient transport in filamentous fungi

Fungi exhibit behaviors distinct from other microbes. Filamentous fungi grow by extending complex networks of branched filaments collectively referred to as the mycelium. These networks can expand over large distances and traverse low-nutrient areas by translocating nutrients through the filament network. This spatial characteristic makes filamentous fungi crucial for soil ecosystems, supporting stable microbial communities and promoting plant growth. However, simulating these behaviors is complex. The elongated nature of fungal compartments results in different mechanical interactions compared to the commonly modeled spherical bacteria. These detailed hyphal mechanics require specialized consideration and are often excluded from conventional fungal simulation packages. Additionally, the extensive fungal networks in nature demand computationally intensive simulations, necessitating high-performance algorithms. Therefore, realistic fungi simulations require specialized software. Here, we introduce a fungal modeling expansion to the high-performance biological modelling and interface exchange (bmx) software suite. bmx leverages adaptive mesh refinement in AMReX for chemical diffusion and incorporates a full mechanical model for bacterial cells, accelerated by GPUs. By extending bmx to model filamentous particles, we demonstrate the formation of complex filament networks through interactions like hyphal branching and fusion (anastomosis). We show that the networks produced match real-world fungal structures through various metrics. This work supports computational studies of fungal growth dynamics and can be adapted to investigate the growth of other filamentous structures in biology or materials science. The expanded-BMX package is open-sourced and is available online.

Cell mechanics↗
NASA NTRS2019

Downscaling and Validation of SMAP Radiometer Soil Moisture in CONUS

The SMAP (Soil Moisture Active/Passive) satellite provides global soil moisture (SM) estimates that can be used for scientific research and applications (such as the hydrological cycle, agriculture, ecology, and land atmosphere interactions). Currently, SMAP provides the enhanced radiometer-only SM product (L2SMP) at 9 km grid resolution. However, this spatial resolution is still not enough to satisfy the needs of some studies that require a finer spatial resolution SM product, particularly in agricultural and watershed applications. This study applied a downscaling algorithm to the SMAP 9 km SM product to produce a 1 km resolution over the CONUS (Contiguous United States). The downscaling algorithm is based on the relationship between temperature change and SM modulated by Normalized Difference Vegetation Index (NDVI) of a given time period. This relationship was modeled using variables derived from NLDAS (North America Land Data Assimilation System) and NASA's LTDR (Land Long Term Data Record) between 1981-2018. The algorithm was implemented uses the 1 km MODIS Aqua LST (Land Surface Temperature) product. The downscaled SMAP 1 km SM was validated using in situ SM measurements from the ISMN (International Soil Moisture Network). The validation metrics show an improved overall accuracy of the downscaled SM.

downscaling↗
NASA NTRS2002

Analysis of Salinity Intrusion in the San Francisco Bay-Delta using a GA- Optimized Neural Net, and Application of the Model to Prediction in the Elkhorn Slough Habitat

The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Burman Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Burman Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located alone the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay/Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with this procedure is that the numerical simulation takes roughly 16 hours to complete a C:~ prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Burman Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the system is strongly tidal driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects, and used this to enhance the neural network by mapping input-output relationships in a more efficient manner. Furthermore, the neural network implicitly incorporates both the hydrodynamic and water quality models into a single predictive system. Although our model has not yet been enhanced to demonstrate improve pumping schedules, it has the possibility to support better decision-making procedures that may then be implemented by State agencies if desired. Our intention is now to use this model in the smaller Elkhorn Slough complex near Monterey Bay where no such hydrodynamic model currently exists. At the Elkhorn Slough, we are fusing the neural net model of tidally-driven flow with in situ flow data and airborne and satellite remote sensation data. These further constrain the behavior of the model in predicting the longer-term health and future of this vital estuary.

Thompson, David E.↗
NASA NTRS2007

Modeling Malaria Transmission in Thailand and Indonesia

Malaria Modeling and Surveillance is a project in the NASA Applied Sciences Public Health Applications Program. The main objectives of this project are: 1) identification of the potential breeding sites for major vector species: 2) implementation of a malaria transmission model to identify they key factors that sustain or intensify malaria transmission; and 3) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity. Remote sensing and GIs are the essential elements of this project. The NASA Earth science data sets used in this project include AVHRR Pathfinder, TRMM, MODIS, NSIPP and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of precipitation, temperatures, humidity and vegetation. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Examples for spatio-temporal modeling of malaria transmissions in Southeast Asia are given. Discrete event simulations were used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to Southeast Asia. The model and techniques are equally applicable to other regions of the world, when appropriate epidemiological and vector ecological parameters are used as input.

Kiang, Richard↗
DOE OSTI2025

Molecular diversity of dissolved organic matter reflects macroecological patterns in river networks

Deciphering dissolved organic matter (DOM) molecular complexity is crucial for understanding ecosystem function. Using the continental-scale Worldwide Hydrobiogeochemistry Observation Network for Dynamic Rivers Systems (WHONDRS) Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) dataset, we reveal fundamental scaling patterns of DOM chemodiversity with watershed characteristics. Analysis of 54 river sites shows local and regional watershed features significantly influence DOM chemodiversity (2500–8718 unique formulae), exhibiting consistent scaling patterns across compound classes and a novel latitudinal gradient (decreasing diversity with increasing latitude). Scaling relationships for DOM composition vary by compound class. Crucially, the scaling parameters (B, baseline chemodiversity; Z, sensitivity) are linearly interrelated. This B–Z relationship is most robust for potentially bio-labile carbohydrates (coefficient of determination R 2 ≈ 0.85), diminishing for recalcitrant, plant-derived molecules (such as lignin), and indicates (potential) biolability-dependent coupling between baseline diversity and environmental responsiveness. These quantitative scaling relationships, with scaling exponents ranging from − 2.1 to 2.2 across compound classes, enable prediction of DOM composition across watersheds, offering a framework to understand ecosystem responses to environmental change. This research bridges biogeochemistry and ecology, providing tools to anticipate molecular transformations across scales.

59 BASIC BIOLOGICAL SCIENCES↗