Browsing by Author "Figueiredo, Renato J."

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    Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting
    Carey, Cayelan C.; Woelmer, Whitney M.; Lofton, Mary E.; Figueiredo, Renato J.; Bookout, Bethany J.; Corrigan, Rachel S.; Daneshmand, Vahid; Hounshell, Alexandria G.; Howard, Dexter W.; Lewis, Abigail S. L.; McClure, Ryan P.; Wander, Heather L.; Ward, Nicole K.; Thomas, R. Quinn (2021-01-18)
    Near-term, iterative ecological forecasts with quantified uncertainty have great potential for improving lake and reservoir management. For example, if managers received a forecast indicating a high likelihood of impending impairment, they could make decisions today to prevent or mitigate poor water quality in the future. Increasing the number of automated, real-time freshwater forecasts used for management requires integrating interdisciplinary expertise to develop a framework that seamlessly links data, models, and cyberinfrastructure, as well as collaborations with managers to ensure that forecasts are embedded into decision-making workflows. The goal of this study is to advance the implementation of near-term, iterative ecological forecasts for freshwater management. We first provide an overview of FLARE (Forecasting Lake And Reservoir Ecosystems), a forecasting framework we developed and applied to a drinking water reservoir to assist water quality management, as a potential open-source option for interested users. We used FLARE to develop scenario forecasts simulating different water quality interventions to inform manager decision-making. Second, we share lessons learned from our experience developing and running FLARE over 2 years to inform other forecasting projects. We specifically focus on how to develop, implement, and maintain a forecasting system used for active management. Our goal is to break down the barriers to forecasting for freshwater researchers, with the aim of improving lake and reservoir management globally.
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    Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward
    Carey, Cayelan C.; Ward, Nicole K.; Farrell, Kaitlin J.; Lofton, Mary E.; Krinos, Arianna, I.; McClure, Ryan P.; Subratie, Kensworth C.; Figueiredo, Renato J.; Doubek, Jonathan P.; Hanson, Paul C.; Papadopoulos, Philip; Arzberger, Peter (Ecological Society of America, 2019-05)
    In the era of big data, ecologists are increasingly relying on computational approaches and tools to answer existing questions and pose new research questions. These include both software applications (e.g., simulation models, databases and machine learning algorithms) and hardware systems (e.g., wireless sensor networks, supercomputing, drones and satellites), motivating the need for greater collaboration between computer scientists and ecologists. Here, we outline some synergistic opportunities for scientists in both disciplines that can be gained by building collaborations between the computer science and ecology research communities, with a focus on the benefits to ecology specifically. We also identify past contributions of computer science to ecology, including high-frequency environmental sensor technology, advanced supercomputing capacity for ecological modeling, databases for long-term and high-frequency datasets, and software programs for ecological analyses, to anticipate future potential contributions. These examples highlight the power and potential for further integration of computer science technology and ideas into the ecological research community. Finally, we translate our own experiences working together as a team of computer scientists and ecologists over the past decade into a conceptual framework with recommendations for supporting productive collaborations at the interface of the two disciplines. We specifically focus on how to apply best practices of team science for bridging computer science and ecology, which we advocate will substantially benefit ecology long-term.
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    FaaSr: Cross-Platform Function-as-a-Service Serverless Scientific Workflows in R
    Park, Sungjae; Thomas, R. Quinn; Carey, Cayelan C.; Delany, Austin D.; Ku, Yun-Jung; Lofton, Mary E.; Figueiredo, Renato J. (IEEE, 2024-09)
    Modern Function-as-a-Service (FaaS) cloud platforms offer great potential for supporting event-driven scientific workflows. Nonetheless, there remain barriers to adoption by the scientific community in domains such as environmental sciences, where R is the focal language used for the development of applications and where users are typically not well-versed with FaaS APIs. This paper describes the design and implementation of FaaSr, a novel middleware system that supports event-driven scientific workflows in R. A key novelty in FaaSr is the ability to deploy workflows across FaaS providers without the need for any managed servers for coordination. With FaaSr: 1) functions are written in R; 2) the runtime environments for their execution are customizable containers; 3) functions access data in cloud storage (S3) with a familiar file-based abstraction supporting both full file put/get primitives and subsetting using the Parquet format; and 4) function invocation and workflow coordination only requires S3 cloud object storage, without relying on any dedicated, active workflow engine server or cloud-specific queues/databases. The paper reports on the functionality and performance of FaaSr for micro-benchmarks and two case studies: event-driven forecast and batch job workflows. These demonstrate the ability to deploy workflows across multiple platforms (GitHub Actions, Amazon Web Services Lambda, and the open-source OpenWhisk), without the need for dedicated coordination servers, across both cloud and edge resources. FaaSr is open-source and available as a CRAN package.
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    FaaSr: R Package for Function-as-a-Service Cloud Computing
    Park, Sungjae; Ku, Yun-Jung; Mu, Nan; Daneshmand, Vahid; Thomas, R. Quinn; Carey, Cayelan C.; Figueiredo, Renato J. (The Open Journal, 2024-11)
    The FaaSr software makes it easy for scientists to execute computational workflows developed natively using the R programming language in Function-as-a-Service (FaaS) serverless cloud infrastructures and using S3 cloud object storage (Amazon, 2024b; MinIO, 2024). A key objective of the software is to reduce barriers to entry to cloud computing for scientists in domains such as environmental sciences, where R is widely used (Lai et al., 2019). To this end, FaaSr is designed to hide complexities associated with using cloud Application Programming Interfaces (APIs) for different FaaS and S3 providers, and exposes to the end user a set of simple function interfaces to: 1) register and invoke FaaS functions, 2) compose functions to create workflow execution graphs, and 3) access cloud storage at run time. The software supports encapsulation of execution environments in Docker images that can be deployed reproducibly across multiple providers: AWS Lambda (Amazon, 2024a), GitHub Actions (Github, 2024), and OpenWhisk (Apache, 2024), where users are able to leverage a baseline image with the widely-used Rocker/Tidyverse runtime (Nüst et al., 2020), as well as customize their execution environment if needed. FaaSr is available as a CRAN package to facilitate its installation in R environments.
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    A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global change
    Carey, Cayelan C.; Calder, Ryan S. D.; Figueiredo, Renato J.; Gramacy, Robert B.; Lofton, Mary E.; Schreiber, Madeline E.; Thomas, R. Quinn (Springer, 2024-09-20)
    Phytoplankton blooms create harmful toxins, scums, and taste and odor compounds and thus pose a major risk to drinking water safety. Climate and land use change are increasing the frequency and severity of blooms, motivating the development of new approaches for preemptive, rather than reactive, water management. While several real-time phytoplankton forecasts have been developed to date, none are both automated and quantify uncertainty in their predictions, which is critical for manager use. In response to this need, we outline a framework for developing the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty, thereby enabling managers to adapt operations and mitigate blooms. Implementation of this system calls for new, integrated ecosystem and statistical models; automated cyberinfrastructure; effective decision support tools; and training for forecasters and decision makers. We provide a research agenda for the creation of this system, as well as recommendations for developing real-time phytoplankton forecasts to support management.
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    GRAPLEr: A Distributed Collaborative Environment for Lake Ecosystem Modeling that Integrates Overlay Networks, High-throughput Computing, and Web Services
    Subratie, Kensworth C.; Aditya, Saumitra; Figueiredo, Renato J.; Carey, Cayelan C.; Hanson, Paul C. (2015-09-29)
    The GLEON Research And PRAGMA Lake Expedition -- GRAPLE -- is a collaborative effort between computer science and lake ecology researchers. It aims to improve our understanding and predictive capacity of the threats to the water quality of our freshwater resources, including climate change. This paper presents GRAPLEr, a distributed computing system used to address the modeling needs of GRAPLE researchers. GRAPLEr integrates and applies overlay virtual network, high-throughput computing, and Web service technologies in a novel way. First, its user-level IP-over-P2P (IPOP) overlay network allows compute and storage resources distributed across independently-administered institutions (including private and public clouds) to be aggregated into a common virtual network, despite the presence of firewalls and network address translators. Second, resources aggregated by the IPOP virtual network run unmodified high-throughput computing middleware (HTCondor) to enable large numbers of model simulations to be executed concurrently across the distributed computing resources. Third, a Web service interface allows end users to submit job requests to the system using client libraries that integrate with the R statistical computing environment. The paper presents the GRAPLEr architecture, describes its implementation and reports on its performance for batches of General Lake Model (GLM) simulations across three cloud infrastructures (University of Florida, CloudLab, and Microsoft Azure).
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    Near-term ecological forecasting for climate change action
    Dietze, Michael; White, Ethan P.; Abeyta, Antoinette; Boettiger, Carl; Bueno Watts, Nievita; Carey, Cayelan C.; Chaplin-Kramer, Rebecca; Emanuel, Ryan E.; Ernest, S. K. Morgan; Figueiredo, Renato J.; Gerst, Michael D.; Johnson, Leah R.; Kenney, Melissa A.; McLachlan, Jason S.; Paschalidis, Ioannis Ch.; Peters, Jody A.; Rollinson, Christine R.; Simonis, Juniper; Sullivan-Wiley, Kira; Thomas, R. Quinn; Wardle, Glenda M.; Willson, Alyssa M.; Zwart, Jacob (Springer Nature, 2024-11-08)
    A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships.
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    Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the US
    Thomas, R. Quinn; McClure, Ryan P.; Moore, Tadhg N.; Woelmer, Whitney M.; Boettiger, Carl; Figueiredo, Renato J.; Hensley, Robert T.; Carey, Cayelan C. (Wiley, 2023-04)
    The US National Ecological Observatory Network's (NEON's) standardized monitoring program provides an unprecedented opportunity for comparing the predictability of ecosystems. To harness the power of NEON data for examining environmental predictability, we scaled a near-term, iterative, water temperature forecasting system to all six NEON lakes in the conterminous US. We generated 1-day-ahead to 35-days-ahead forecasts using a process-based hydrodynamic model that was updated with observations as they became available. Among lakes, forecasts were more accurate than a null model up to 35-days-ahead, with an aggregated 1-day-ahead root-mean square error (RMSE) of 0.61 degrees C and a 35-days-ahead RMSE of 2.17 degrees C. Water temperature forecast accuracy was positively associated with lake depth and water clarity, and negatively associated with fetch and catchment size. The results of our analysis suggest that lake characteristics interact with weather to control the predictability of thermal structure. Our work provides some of the first probabilistic forecasts of NEON sites and a framework for examining continental-scale predictability.
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    A Near-Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time
    Thomas, R. Quinn; Figueiredo, Renato J.; Daneshmand, Vahid; Bookout, Bethany J.; Puckett, Laura K.; Carey, Cayelan C. (2020-11)
    Freshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and deployed a real-time iterative water temperature forecasting system (FLARE-Forecasting Lake And Reservoir Ecosystems). FLARE is composed of water temperature and meteorology sensors that wirelessly stream data, a data assimilation algorithm that uses sensor observations to update predictions from a hydrodynamic model and calibrate model parameters, and an ensemble-based forecasting algorithm to generate forecasts that include uncertainty. Importantly, FLARE quantifies the contribution of different sources of uncertainty (driver data, initial conditions, model process, and parameters) to each daily forecast of water temperature at multiple depths. We applied FLARE to Falling Creek Reservoir (Vinton, Virginia, USA), a drinking water supply, during a 475-day period encompassing stratified and mixed thermal conditions. Aggregated across this period, root mean square error (RMSE) of daily forecasted water temperatures was 1.13 degrees C at the reservoir's near-surface (1.0 m) for 7-day ahead forecasts and 1.62 degrees C for 16-day ahead forecasts. The RMSE of forecasted water temperatures at the near-sediments (8.0 m) was 0.87 degrees C for 7-day forecasts and 1.20 degrees C for 16-day forecasts. FLARE successfully predicted the onset of fall turnover 4-14 days in advance in two sequential years. Uncertainty partitioning identified meteorology driver data as the dominant source of uncertainty in forecasts for most depths and thermal conditions, except for the near-sediments in summer, when model process uncertainty dominated. Overall, FLARE provides an open-source system for lake and reservoir water quality forecasting to improve real-time management.
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    Translational Edge and Cloud Computing to Advance Lake Water Quality Forecasting
    Figueiredo, Renato J.; Carey, Cayelan C.; Thomas, R. Quinn (IEEE, 2024-11-20)
    In this article, we report on our experiences with interdisciplinary projects at the intersection of freshwater ecology, data science, and computer science. The translational research process has progressively led to the development of distributed systems that apply both edge computing and function-as-a-service (FaaS) cloud computing to support end-to-end water quality forecasting workflows across the edge-to-cloud continuum.