Results for “workflow management” — Page 18

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗

NASA NTRS2021

Automated Uas Measurements of Reflectance and Solar Induced Florescence (SIF) for Assessment Of the Dinamics in Photosynthetic Function, Application for Maze (Zea Mays L.) in Greenbelt, Maryland, Us

For accurate assessment of vegetation function there is a strong need for advancement in the remote sensing methods and instruments, to enable monitoring of the parameters governing photosynthesis at the temporal frequency relevant to their dynamics and at a spatial scale that allows practical assessment and management. Currently, no single sensor can provide data at the desired high temporal, spectral and spatial resolutions. Our field measurements obtained using the integrated UAS Piccolo system during the summers of2017 and 2018 demonstrate that science quality reflectance and solar induced fluorescence (SIF) data can be retrieved with high temporal frequency using small Unmanned Aerial Systems (UAS). The implemented approach facilitates data comparisons through space and time, and the integration with other spectral satellite and airborne data. This investigation contributes for bridging the gap in Earth observation between field and airborne measurements by implementing autonomous methods for obtaining high temporal frequency spectroscopy measurements from an Unmanned Aerial Systems (UAS) at various illumination conditions. An advancement in the Intelligent Payload Module (IPM) facilitated the implementation of an optimization workflow to collect spectral data for characterizing vegetation reflectance and solar induced fluorescence (SIF).

Petya Campbell↗

DOE OSTI2026

A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction

In many university and healthcare projects, models are built for very different data types such as tables, institutional time series, and medical images, but they are deployed as separate applications. In this work, that separation made testing and maintenance difficult because each module had its own pipeline and runtime requirements. This paper presents an integrated AI lakehouse-style implementation that runs three model pipelines inside one containerized backend. For medical imaging, we used MRI datasets from IEEE DataPort: a four-class classification set with 7012 images (5708 train/1304 test) and a segmentation set with 3063 image–mask pairs. The classification model (ResNet50 transfer learning) is evaluated using a proper train–validation–test protocol across multiple splits (80/10/10, 70/10/20, 60/10/30, and 10/30/60), achieving a test accuracy of 99.00% under the standard 80/10/10 split. Additionally, a patient-level evaluation is conducted using an external glioma dataset to provide a more realistic assessment without data leakage. The segmentation model (DeepLabV3-ResNet50) achieved 83.09% validation mIoU and 88.79% Dice score. For university KPI forecasting, we used annual IPEDS and NSF HERD data from 2010 to 2023 for three universities (BSU, EOU, and UAB). To examine the effect of preprocessing on forecasting performance, two case studies are conducted. In the first case, linear interpolation is applied to generate semester-level data. In the second case, the original annual data is used directly without interpolation. Random Forest regression and ARIMA models are evaluated using MAE, RMSE, MAPE, and R 2 . The results showed that interpolation improved apparent forecasting performance due to smoothing, while evaluation on the original annual data provided a more realistic assessment of model behavior. To further validate the framework on a larger dataset, an additional case study is conducted using a student dropout dataset. For water potability, we trained and compared multiple tabular classifiers on a large dataset (1,048,575 samples). A Random Forest model (100 trees, max depth 10) achieved 85.86% test accuracy and high recall for unsafe samples (0.8447). All modules are served via FastAPI and deployed together using Docker, with workflow automation routing requests to the correct endpoint. System-level benchmarking indicates that the backend maintains stable throughput and latency under concurrent requests.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

An Innovative High Throughput Genome Releaser for Rapid and Efficient PCR Screening

High-throughput PCR screening is vital in synthetic biology and metabolic engineering as it allows researchers to rapidly analyze and detect numerous targeted genetic mutation in the genome. Current challenges for high-throughput PCR screening in synthetic biology include efficiently preparing genomic DNA, optimizing protocols for diverse sample types, managing contamination risks, and effectively analyzing the large volumes of data generated while ensuring consistent and accurate results. In this study, we present the development of a High Throughput Genome Releaser (HTGR), an innovative device addressing common challenges in screening PCR. This genome DNA releaser is designed based on a squash method for rapid, cost-effective, and efficient DNA release, optimized for subsequent PCR reactions. After experimenting with various synthetic materials, we selected a plastic that closely replicates the smooth surface and compression properties of microscope slides, ensuring reliable performance. We engineered a device featuring a 96-Well Plate and a shear applicator, operable both manually and automatically, and compatible with standard liquid-handling robot platform. This compatibility enhances ease of use in high-throughput PCR workflows. Additionally, we developed software to support its automatic functions. Our results demonstrated that the specially engineered 96-Well Plate and HTGR can effectively squash fungal spores , which release enough genome DNA for PCR screening. The genome releaser facilitates the preparation of PCR-amplifiable genomic DNA substrate from 96 samples within minutes, eliminates the need for extraction buffers, and is adaptable to a wide range of microorganisms and cells, which could significantly advance biomanufacturing processes.

Yuan, Guoliang [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2025

Land-use analysis using infrastructure representations and high-resolution flood inundation mapping techniques

In the face of climate change and population growth in coastal regions, land-use analysis efforts are more challenging than ever. Land-use decision-makers in coastal communities are burdened with the difficult choices of where to place new homes versus other assets. While there has been an increased focus on hazard mitigation and disaster resilience in the field of planning, evidence points towards continued development in risk-prone areas including flood zones. Residential development within flood zones specifically continues to be a major issue. To help counter this trend, this study introduces a novel land-use analysis method, coupling topographic flood inundation mapping techniques with digital elevation model (DEM) adaptations. This Topographic Model Scenario Generation workflow can be used by planners early in the land-use decision making process and provides an alternative to high-computational hydraulic models. The analysis also includes the identification of strengths and weaknesses of topographic models' recognition of built infrastructure assets, adding to a limited body of knowledge addressing recommended uses of such models. Levees and canals prove particularly functional in this context while detention ponds less so, likely due to a lack of total water mass accountability. Lastly, we provide a functional demonstration in Southeast Texas to illustrate the workflow's ability to create multiple infrastructure scenarios and visualize their effects across different flood events.

42 ENGINEERING↗

DOE OSTI2024

Investigation of acoustic waves under subsurface conditions to improve the predictions of rock mechanical properties and natural fracture characteristics

Mechanical properties and natural fracture characteristics are critical to investigate for subsurface engineering applications, including carbon storage, well drilling, and stimulation, as they govern rock stability, fluid flow, and mechanical behavior under stress. This dissertation integrates experimental and machine learning approaches to enhance the prediction and understanding of these properties by analyzing acoustic wave behavior under varied subsurface conditions. First, the influence of temperature, pore pressure, and supercritical CO2 (scCO2) saturation on poroelastic properties is examined using Gray Berea sandstone samples. The results show that temperature and pore pressure significantly affect the bulk modulus and Biot’s coefficient, while scCO2 saturation impacts rock compressibility, informing strategies for effective geological carbon storage. The study extends this understanding by experimentally evaluating the impact of reservoir depletion on the dynamic mechanical properties of the emerging Caney shale in South Oklahoma with the employment of unsupervised machine learning to predict static mechanical properties across the Caney shale. Integrating petrophysical data and chemostratigraphy, the workflow—featuring K-means clustering, principal component analysis (PCA), and inverse distance weighting (IDW)—improves stratigraphic characterization and the estimation of static-to-dynamic modulus ratios, which is vital for optimizing drilling and stimulation strategies. Finally, the work explores how natural fracture characteristics in shale influence acoustic waveforms and shear wave splitting (SWS) analysis. Experimental data on fractured samples under different stress and temperature conditions, combined with machine learning models such as K-nearest neighbors (KNN) and extreme gradient boosting (XGBoost), reveal key fracture properties impacting SWS and wave propagation. Together, these studies provide a comprehensive framework for linking acoustic wave behavior with rock properties, advancing the methods for monitoring and predicting geomechanical changes. The insights offered valuable implications for safer, more efficient CO2 injection, hydrocarbon extraction, and subsurface management.

Elkholy, Sherif↗

DOE OSTI2025

Toward a persistent event-streaming system for high-performance computing applications

High-performance computing (HPC) applications have traditionally relied on parallel file systems and file transfer services to manage data movement and storage. Alternative approaches have been proposed that use direct communications between application components, trading persistence and fault tolerance for speed. Event-driven architectures, as popularized in enterprise contexts, present a compelling middle ground, avoiding the performance cost and API constraints of parallel file systems while retaining persistence and offering impedance matching between application components. However, adapting streaming frameworks to HPC workloads requires addressing challenges unique to HPC systems. This paper investigates the potential for a streaming framework designed for HPC infrastructures and use cases. We introduce Mofka, a persistent event-streaming framework designed specifically for HPC environments. Mofka combines the capabilities of a traditional streaming service with optimizations tailored to the HPC context, such as support for massively multicore nodes, efficient scaling for large producer-consumer workflows, RDMA-enabled high-performance network communications, specialized network fabrics with multiple links per node, and efficient handling of large scientific data payloads. Built using the Mochi suite of HPC data service components, Mofka provides a lightweight, modular, and high-performance solution for persistent streaming in HPC systems. We present the architecture of Mofka and evaluate its performance against Kafka and Redpanda using benchmarks on diverse platforms, including Argonne's Polaris and Oak Ridge's Frontier supercomputers, showing up to 8× improvement in throughput in some scenarios. We then demonstrate its utility in several real-world applications: a tomographic reconstruction pipeline, a workflow for the discovery of metal-organic frameworks for carbon capture, and the instrumentation of Dask workflows for provenance tracking and performance analysis.

HPC↗

NASA NTRS2016

Climate Analytics as a Service

Exascale computing, big data, and cloud computing are driving the evolution of large-scale information systems toward a model of data-proximal analysis. In response, we are developing a concept of climate analytics as a service (CAaaS) that represents a convergence of data analytics and archive management. With this approach, high-performance compute-storage implemented as an analytic system is part of a dynamic archive comprising both static and computationally realized objects. It is a system whose capabilities are framed as behaviors over a static data collection, but where queries cause results to be created, not found and retrieved. Those results can be the product of a complex analysis, but, importantly, they also can be tailored responses to the simplest of requests. NASA's MERRA Analytic Service and associated Climate Data Services API provide a real-world example of climate analytics delivered as a service in this way. Our experiences reveal several advantages to this approach, not the least of which is orders-of-magnitude time reduction in the data assembly task common to many scientific workflows.

big data↗

NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗

NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗

DOE OSTI2024

Sustainable Enablers of Knowledge Management Strategies in a Higher Education Institution

By facilitating the capture, organization, and dissemination of knowledge within and beyond the institution, knowledge management (KM) in higher education institutions (HEIs) fuels innovation, enhances research impact, and strengthens collaboration, ultimately leading to the creation of new knowledge and its valuable exchange. However, there is still much to explore in terms of the enablers of knowledge creation, sharing, and transfer. Therefore, this paper aims to identify the enablers of effective KM in the Polytechnique University of Leiria, which serves as a benchmark for other higher education institutions due to its leadership role in RUN-EU, a consortium of European universities. To achieve this, a narrative analysis based on information from SCOPUS and the institute’s website, focusing on innovation, research, and development strategies, is proposed. The findings suggest that for KM initiatives to be successful, they need to be strategically designed, culturally supported, technologically enabled, and integrated into existing workflows.

Santos, Eleonora (ORCID:0000000346930804)↗

DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗

DOE OSTI2024

ANS Winter 2024 Summary: MCCAFE: The Monte Carlo Constructor for ATR Fuel Elements

The Irradiation Experiment Neutronics Analysis Department at Idaho National Laboratory (INL) has implemented a new analysis workflow for experiments in the Advanced Test Reactor (ATR). One key piece of this workflow is the Monte Carlo Constructor for ATR Fuel Elements, or MCCAFE. For each ATR operating cycle, the Reactor and Nuclear Safety Engineering (RNSE) Department first solves the core in eigenvalue mode and depletes the driver fuel materials. In a separate calculation, neutronics analysts model and deplete the materials of one or more irradiation experiments, usually in a series of fixed-source Monte Carlo N-Particle (MCNP) models of the ATR for neutron transport calculations. It was desirable to use the results of the former calculations to inform the models of the latter. MCCAFE is a Python program developed using American Society of Mechanical Engineers Nuclear Quality Assurance-1 procedures at INL. Its purpose is to take the calculated results from the RNSE depletion solutions and the measured or projected operating parameters from the Nuclear Data Management and Analysis System (NDMAS) to generate fixed-source models of the ATR core at given points in time across one or more cycles.

99 - GENERAL AND MISCELLANEOUS↗

NASA NTRS1992

Ground Systems Development Environment (GSDE) software configuration management

This report presents a review of the software configuration management (CM) plans developed for the Space Station Training Facility (SSTF) and the Space Station Control Center. The scope of the CM assessed in this report is the Systems Integration and Testing Phase of the Ground Systems development life cycle. This is the period following coding and unit test and preceding delivery to operational use. This report is one of a series from a study of the interfaces among the Ground Systems Development Environment (GSDE), the development systems for the SSTF and the SSCC, and the target systems for SSCC and SSTF. This is the last report in the series. The focus of this report is on the CM plans developed by the contractors for the Mission Systems Contract (MSC) and the Training Systems Contract (TSC). CM requirements are summarized and described in terms of operational software development. The software workflows proposed in the TSC and MSC plans are reviewed in this context, and evaluated against the CM requirements defined in earlier study reports. Recommendations are made to improve the effectiveness of CM while minimizing its impact on the developers.

Church, Victor E.↗

DOE OSTI2025

eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework

Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites. To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods. We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables. eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.

Keller, Abigail G. [Department of Environment Scie↗

NASA NTRSDate not supplied

Addressing User Needs through the Stakeholder Engagement Program

Every two years, the Satellite Needs Working Group (SNWG), an initiative of the U.S. Group on Earth Observations (USGEO), surveys federal agencies to pinpoint their satellite Earth observation needs. For each expressed need, NASA-led assessment teams coordinate with the agencies to devise solutions. Solutions can include existing or modified data products as well as the construction of new data products and technologies, such as the Harmonized Landsat Sentinel-2 (HLS) product and the Catalog of Archived Sub-Orbital Earth Science Investigations (CASEI). To facilitate adoption of new data products and technologies, the SNWG Management Office’s Stakeholder Engagement Program (SEP) was established. The program’s primary goals are to respond to training and capacity building needs expressed by agencies and to encourage engagement from stakeholders as SNWG solutions are developed. To serve these needs, SEP has developed the following: an SNWG Solutions Earthdata webpage, an SEP Earthdata webpage, and an Earthdata Search Portal for SNWG products. These avenues provide assistance to users from all backgrounds and levels of expertise as well as publicize the ongoing efforts of SNWG solutions. In addition, the SEP is also collaborating with NASA’s Short-term Prediction Research and Transition (SPoRT) Center to develop user-driven applications for SNWG products leveraging stakeholder input. This presentation will provide an overview of the SEP, highlight the resources currently available to users, and describe ongoing efforts to address the needs of users, so SNWG products can be better implemented into scientific workflows.

Jenny Wood↗

NASA NTRS2023

Shoshone River Water Resources II: Quantifying Sediment Input in the Shoshone River in Wyoming using the Soil and Water Assessment Tool for Enhanced Water Quality Monitoring

The Willwood Dam, an irrigation diversion dam located on the Shoshone River (Wyoming, USA), has faced ongoing issues with sediment accumulation and needs frequent sediment flushing to remain operable. However, high suspended sediment levels during flushing events have negatively impacted downstream aquatic ecology and recreational opportunities. To address these problems, DEVELOP partnered with the Wyoming Department of Environmental Quality, Shoshone River Partners, and United States Geologic Survey. During term one, the team developed a workflow to map turbidity using PlanetScope imagery, analyze time series precipitation data, and create landcover maps. For this term, we focused on three methods to gain a better understanding of sediment sources: 1) improving remote sensing of turbidity, 2) modeling sediment transport within the watershed using the Soil and Water Assessment Tool (SWAT), and 3) conducting a snow cover time series analysis using Suomi NPP VIIRS imagery. Through remote sensing, we found Dry Creek/Homesteader Creek and Penney Gulch/Rough Gulch had the highest concentration sediment plumes. The SWAT+ model created a high-resolution grid model of the watershed, identifying high sedimentation in the western and southern subbasins but displayed low-correlated calibration and validation results due to limited observed data. We analyzed the snow cover extent alongside other hydrologic variables and found that snow melt events correlated with increases in suspended sediment concentration and turbidity values. Coupling remote sensing with hydrological modeling will give watershed managers a new perspective on high-priority regions for implementation of remediation to reduce the sediment build-up at the Willwood Dam.

Suspended Sediment↗