Title: Next Generation Ensembled-Derived Precipitation Estimate for River Forecast Centers

Project Lead: Pat Clemins

Abstract: The River Forecast Centers (RFCs) produce gridded quantitative precipitation estimates (QPEs) that are used as forcing data in several applications, including RFC hydrologic models and the National Water Model. RFC forecasters create this QPE product using the Multi-Sensor Precipitation Estimator (MPE) by combining precipitation estimates from several different sources, including Multi-Radar Multi-Sensor (MRMS), precipitation gauges, radar estimates, and satellite estimates. The MPE software has not been significantly updated in several decades and it is a time-consuming process for RFC forecasters to produce a quality product, especially in regions with poor radar and precipitation gauge coverage. This project, a collaboration between the RFCs and the University of Vermont (UVM), will create modular data fusion tools that can be combined by RFC forecasters in a web-based tool to create precipitation estimate (NGPE) workflows. Some of these data fusion tools will utilize deep learning algorithms. This more sophisticated, model-driven framework of data fusion tools and configurable workflows will improve the efficiency and accuracy of the QPE by allowing RFC forecasters to build off prior workflows and utilize regional and local observed datasets more effectively. The modular, open-source design of the NGPE workflow will allow the broader hydrologic community to contribute new datasets and tools to the NGPE and create modules to format the output QPE for any hydrologic modeling framework they choose, including NextGen. Finally, increasing the accuracy of the QPE will lead to better hydrological modeling and prediction.

Title: Identifying the usefulness of flood forecast attributes in support of impact-based decision-making

Project Lead: Anne Jefferson

Abstract: Effective flood forecasts that minimize loss of life and property require reaching diverse end users with information relevant to their specific decision-making needs and actions. Previous CIROH-funded research by this team investigated flood warning communication pathways and factors influencing protective action-taking in response to warnings. Building on this work, the goal of this project is to identify flood forecast attributes most relevant to decision-making across a broad range of end users, including both technical information (e.g., uncertainty, timeliness) or non-technical factors (e.g.,

trust, method of delivery). This project evaluates stakeholder preferences for NWS flood forecast briefings through interviews with NOAA staff and Vermont emergency managers, then generates simulated briefings with Flood Inundation Mapping (FIM) information refined through collaborative sessions. Simultaneously, the team will analyze social media engagement with forecast products,and pilot two novel survey methods aimed at collecting real-time data on forecast use by NWS core partners and the public. The project will produce recommendation memos for NOAA, 2+ manuscripts and conference presentations, train 1 post-doctoral researcher, and share results with communities at risk of flooding. The interdisciplinary research team will synthesize lessons learned to plan future research across multiple geographies and to provide operational improvement recommendations to NOAA for enhancing impact-based decision-making for a range of audiences.

Title: Assess the Effectiveness of Communication of Total Water Level Visualizations and Conduct User Testing of Compound Flood Products

Project Lead: Anne Jefferson

Abstract: The Total Water Level (TWL) product provides forecasts of compound flood risk in coastal communities by combining multiple environmental factors, such as tides, storm surge, sea level, waves, and inland flooding. Currently, due to the complexity of this data tool, the best way to present TWL remains an open research question. Developing and refining this product is crucial for accurately estimating flood inundation extent and integrating results into NOAA’s Digital Coast. These data support improved communication, preparedness, and decision-making among coastal emergency managers, and other public coastal stakeholders during coastal flood events. However, TWL data products are complex and can be difficult for non-technical users to interpret accurately and use effectively. This challenge creates research and operational gaps in understanding whether TWL products are designed and communicated in ways that promote clear and accurate interpretation, usability, and timely preparedness. This project addresses that gap by evaluating how different users—technical experts, coastal planners, and community stakeholders—understand and use TWL products. 

The intended outcomes include:

 ● User-tested, evidence-based design recommendations and peer-reviewed publications to improve TWL visualization, clarity, understandability, and usability prior to public release. 

● TWL training curriculum for integration into National Weather Service and FIM programs that strengthen NOAA’s and local emergency managers’ abilities to prepare for and respond to coastal flood risks. 

Ultimately, this work will advance research on communicating and interpreting compound flood forecasts, enabling users to make more timely and confident decisions given actionable flood data, as well as generating practical, decision-relevant insights for NOAA and its partners. By improving how complex flood information is presented and understood, the project supports NOAA’s and CIROH’s broader goals of science-based communication, climate resilience, and public safety.

Title: Expanding the NWM FIM to Support Infrastructure and Impact Using Multi-Model and Improved Visualization and Communication of Actionable Intelligence in Flood Early Warning: NextGen Flood Mapping Intelligence

UVM Project Lead: Anne Jefferson

Abstract: Flood information only matters when it moves people to act. This project expands the impact of the National Water Model’s Flood Inundation Mapping (FIM) by linking multi-model hydraulics, infrastructure intelligence, and evidence-based visualization into a harmonized, decision-ready system. A refined common data model will integrate outputs from HEC-RAS 2D, SRH-2D, and other FIM-producing models, enabling linkage to the NWM hydrofabric and integration with the base NWM FIM. Automated workflows for model generation and flood map production will enhance national capability and improve understanding, use, and trust of NWM FIM products by end users. We will connect flood depth and velocity data to infrastructure impacts, advancing quantitative risk assessment for transportation and other community assets. The project’s visualization research, grounded in cognitive and social science, will design and test communication strategies that improve user comprehension and confidence in multi-model, probabilistic flood impact forecasts. Together, these innovations will make NWM FIM more accessible, transparent, and actionable, bridging science, technology, and society to reduce flood risk nationwide.

Title: Exploring Critical Attributes of 3D Channels for Enhanced Probabilistic Flood Inundation Mapping
 
Project Lead: Rebecca Diehl

Research Team: David Baude

University of Vermont Research Plan: Flood inundation mapping (FIM) represents an essential planning and risk assessment tool produced by the National Water Center and a key product from the National Water Model (NWM). During forecasted high flow events, FIM is generated parsimoniously through the use of the Height Above Nearest Datum and relies on a deterministic synthetic rating curve (SRC) to map discharge to stage. This 2-year project is in collaboration with Colin Phillips and Belize Lane at Utah State University and will enhance the accuracy of FIM through the incorporation of hydraulic and river corridor terrain variability through the development of a probabilistic SRC. The probabilistic SRC will form the hydraulic basis for generating probabilistic FIM within the NextGen NWM. The incorporation of variability and uncertainty into FIM visualizations can highlight the risk within forecasted FIM and potentially reduce the risk of natural flood hazards. 

Title: Developing and Integrating a Social Water Use Model to Improve the Predictive Capacity of Water Resources Models to Account for Water Uses and Sector Tradeoffs
 
Project Lead: Asim Zia

Research Team: Scott Turnbull, Patrick, Clemins, Carina Manitius, Kevin Andrew, Rakhshinda Bano 

University of Vermont Research Plan: Urbanization and intensification of agriculture alter water resources with respect to quality, quantity, and demand. Increasing frequency and intensity of floods and droughts exacerbates water management challenges. Predictive water models are used to support management decisions, but without accounting for human decision-making and subsequent actions, model outputs can be unreliable. Agent based models represent a way to incorporate agency and decision-making into models, both at the individual and institutional level. The goal of this project is to develop a prototype coupled natural and human systems (CNHS) model that simulates community- and sector-level decision-making regarding municipal and agricultural water use at watershed and basin scales. A behavioral theory-agnostic Agent Based Model (ABM) will be integrated with the next-generation national water model (Next-Gen) in this novel CNHS approach to generate realistic scenarios of water use forecasts and hindcasts at sub-watershed and watershed scales. This coupled model will be piloted to simulate and project future water use scenarios under alternate climate change induced extreme event projections for three focal watersheds: Lake Champlain/Richelieu basin; Milk River basin; and San Jacinto River basin. Using a genetic algorithm as a wrapper, the resulting CNHS model will be interrogated with various user-defined scenarios to explore sensitivity and robustness as a basis for transition to a process-based operational model development pathway for informing the configuration of the Next-Gen. This project's outcomes are critical to advance adaptive management of water resources across sectors and communities in the face of increasing frequencies and intensities of extreme events (floods, droughts, heatwaves etc.). Integration of US/Canadian expertise in complementary ways also serves to incorporate transboundary contexts. 

Title: Advancing Water Quality Monitoring and Prediction Capability of USGS NGWOS Program with Satellite and Drone Remote Sensing Technologies
 
This project is part of the multi-institutional project:
Advancing Water Quality Monitoring and Prediction Capability of USGS NGWOS Program with Satellite and Drone Remote Sensing Technologies 
 
Project Lead: Andrew Schroth

University of Vermont Research Plan: Researchers at UVM will collect water samples at least 6 times per year under different seasonal and flow conditions in consultation with the University of Alabama team at predetermined locations. Those predetermined locations include Lake Champlain-Missisquoi Bay, Lake Champlain-St. Albans Bay, Lake Champlain (Main Lake), Lake Champlain-South Lake, Lake Carmi, the Missisquoi River (Swanton, VT), Winooski River (Essex, VT), CT River (West Lebanon, NH). All river locations are co-located with USGS monitoring infrastructure and lake sampling locations are co-located with Vermont Department of Environmental Conservations Long-Term Monitoring Program. Three of the lake sampling stations (Lake Carmi, Missisquoi Bay, Saint Albans Bay) will also be co-located with UVM’s YSI monitoring platforms that will be collecting high-frequency YSI EXO2 sonde profiles from the surface to a half meter from the bottom from roughly May through October. Surface profile measurements will provide high-frequency time series to further inform relationships between sonde turbidity, CDOM, and Chl-A/Phycocyanin fluorescence relative to satellite data. At each water sampling event and location, triplicate manual YSI EXO2 sonde measurements will also be collected that include turbidity, CDOM, and ChlA fluorescence. All water samples will be taken back to the University of Vermont and analyzed in PI Schroth’s laboratory for suspended sediment and DOC concentration (Shimadzu) by technician Blocher, as well as ChlA concentration and phytoplankton biomass and community composition in Co-PI Morales-Williams laboratory by the GRA and Co-PI. UVM researchers will participate in quarterly (or more frequently as needed) remote meetings with the University of Alabama research team and contribute to data analysis and publication generation. In Project Year 1, Co-PI Morales-Williams will host team members from the University of Alabama in her lab to establish comparable phytoplankton methodologies across the research team.
 
Publications, Presentations and Posters

Posters
Liu, H., S. Cohen, Y. Lu, A. Schroth, M. Morales-Williams, A. . Zia, L. Wang, R. Beck, S. Tong, J. Eggleston, T. King. (2024). ). Applications of Autonomous Surface Vehicle (ASV) and Unmanned Aerial System (UAS) in Monitoring River and Lake Water Quality: Challenges, Lessons, and Prospects. AWRA 2024 Spring Conference ; Tuscaloosa, AL;

Title: Developing a Snow Modeling Foundation for Underrepresented Cold Regions in the Northeastern US
 
This project is part of the multi-institutional project:
Advancing CONUS-scale Operational Snow Modeling Capabilities
 
Project Lead(s): Dr. Katherine Hale and Dr. Beverley Wemple

University of Vermont Research Plan: UVM will be primarily responsible for compiling the required forcing and model validation data in historically underrepresented snow-affected regions of the Northeastern USA to better inform the NextGen NWM and thus provide more accurate forecasting of snowpack water resources across the region. UVM will work with the U of U team to generate model inputs for a process-based snow model, iSnobal, across identified testbeds. Representation of the Northeastern snowpack will be determined by comparing model outputs to a newly developed observation network in Vermont (including meteorological stations, snow scales, and UAV flights), funded through the Cold Regions Research and Engineering Lab (CRREL). We will leverage this new snow observatory network in Vermont to test iSnobal. We will focus on model performance across a range of snow accumulation and snowmelt conditions, including rain-on-snow events. The Ranch Brook watershed (9.84 km2, USGS 04288230), a forested alpine catchment on the eastern slopes of Vermont’s highest peak, will serve as our regional testbed, as it contains 13 meteorological stations which also monitor select snowpack characteristics (e.g., depth, melt, SWE), arrayed along elevational and aspect gradients. This testbed offers opportunities to extend this project and link our snow modeling advances to streamflow predictions in the NextGen framework. UVM will then develop a corresponding work plan for integration of a process-based snow modeling approach into NextGen, by identifying (1) forcing data needs to operationalize iSnobal for the Northeast, where snow observations are more limited (than the western US), (2) identify computational barriers to operationalization, and (3) build the BMI architecture for integration of iSnobal into NextGen for our testbed setting. 
 
As an associated activity to this core research, UVM will lead the development of a concept paper for an eventual CIROH Summer Institute on cold regions processes. The concept paper will identify qualified instructors, modeling challenges, and testbed sites. We envision building relationships with other CIROH-supported projects on cold-regions processes to develop this concept paper and identify key resources.
 
Publications, Presentations and Posters

Conference Presentations
Hale, K., Wemple, B., Shanley, J., Grunes, A., Bomblies, A.. (2023). Characterizing the Vermont Snowpack. ESC-2023 Eastern Snow Conference; Easton, PA; https://static1.squarespace.com/static/58b98f7bd1758e4cc271d365/t/65b12ffa2ee1614019e0d237/1706110970182/61.Hale1_ESC23.pdf

DiPietro, T., Chow, C., Grunes, A., Wemple, B., Hale, K.. (2024). Modeling of Snowpack in The Northeast Using iSnobal. CIROH Training and Developers Conference 2024; Salt Lake City, UT; https://ciroh.ua.edu/abstracts/modeling-of-snowpack-in-the-northeast-using-isnobal/

Posters
DiPietro, T., Hale, K., Wemple, B.. (2024). Advancing CONUS-Scale Operational Snow Modeling Capabilities. UVM CIROH All Hands Meeting 2024; https://www.uvm.edu/ciroh/sje/docs/2024-CIROH-All-Hands/UVM-2024-CIROH-All-Hands-Agenda_09Aug2024.pdf

Chow, C., DiPietro, T., Grunes, A., Wemple, B., Hale, K.. (2024). Modeling of Snowpack in The Northeast Using iSnobal. WSC 2024 Western Snow Conference ; Corvallis, OR ; https://westernsnowconference.org/wp-content/uploads/2024_WSC_DRAFT_AGENDA.pdf

Hale, K., A. Schroth, J. Shanley, B. Wemple. (2023). Warmer, dynamic winters drive declines in snowpack and coupled increases in annual and seasonal runoff in a headwater catchment of northeastern USA. AGU Fall Meeting 2023; San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1347688

Title: Audience Segmentation to Improve Flood Inundation Mapping: Engagement and Testing with Technical Users and Impacted Communities
 
This project is part of the multi-institutional project:
Audience Segmentation to Improve Flood Inundation Mapping: Engagement and Testing with Technical Users and Impacted Communities 
 
Project Lead:  Dr. Anne Jefferson  
 
University of Vermont Research Plan: Despite the importance of flood forecasts to support event response and risk reduction actions, there has not been a synthesis of existing flood visualizations and testing of the prototype Flood Inundation Mapping (FIM) with different end-user groups. The goal of this project is to engage and test FIM approaches with technical users and impacted communities. The project will result in publications, presentations/briefings, and operational recommendations to improve the translation and use of FIMs. Intellectual merit: develops a novel, empirically based testing procedure to provide operational improvements that will enhance FIM forecast graphics interpretation and use in decisions. Broader Impacts: relationship building with underserved Tribes and accessibility and compliance consistent with Executive Orders (EOs), memorandum, and regulations.
 
Work to be Completed:  
 
Objective 1: Indigenous Relationship Building and Communication Strategy: For visualizations available to Tribal Government decision makers, tribal emergency managers, and tribal community leaders, we will engage those users to assess whether they meet community expectations, such as integrating Traditional Ecological Knowledge and/or supporting the Federal trust responsibility to these communities. In Year 1, University of Vermont (UVM) will leverage the NOAA-funded Sea Grant network of resilience educators to identify where existing Sea Grant-tribal relationships have included dialogue and successful communication strategies related to inland/riverine flooding. In Year 2, UVM will contribute to the development of a strategy for flood communication to support NOAA FIM service equity priorities.  
 
Objective 2: Interviews with FIM Producers and Power Users: We will facilitate semi-structured interviews through group meetings, in coordination with NOAA, with the FIMs producers and power users to better understand - from their perspective - the goals of the products, identify primary and secondary audiences of the products, and determine any design flexibility that needs to be considered as a result of planned or potential product modifications. In Year 1, UVM will contribute to participant recruitment for interviews with power users. UVM will also facilitate collaboration and information sharing with other CIROH-funded social science teams conducting focus groups, surveys, or interviews related to flood forecast communications/graphics.  
 
Objective 3: Synthesize and Diagnose Flood Visualizations: To understand the visualization and audience utilization of existing flood forecast products, we will conduct an analysis of existing literature and products to synthesize and diagnose communication challenges associated with wide-ranging approaches to flood visualizations and decision support tools.  In Year 1, UVM will play a leading role in the identification and synthesis of existing flood forecast products, drawing on the PI’s training and connections within the hydrologic science community. The flood resilience educator’s contributions will focus on flooding-related visualizations used by the Sea Grant network and its partners. UVM will contribute to the assessment and diagnosis of the existing visualizations, under the leadership of University of Minnesota (UMN). This work may continue into Year 2, in order to develop a journal article for peer-reviewed publication.  
 
Objective 4: Visualization Testing: Using a prioritized subset of diagnosed visualization problems from Objective 3, a multi-step co-production process will be used to test the efficacy of alternative prototypes and generate empirical results. In Year 1, UVM’s PI will participate in collaborative discussions with UMN and NOAA planning the testing. In Year 2, UVM’s PI will participate in discussions during and following testing. UVM’s participation in these discussions will enable greater contribution to Objective 5.  
 
Objective 5: Synthesizing results and developing best practices recommendations: Given the results of the interviews on user needs, synthesis and diagnosis of existing and new flood forecast visualizations, and potential redesign of the visualizations, we will synthesize the results into peer-reviewed papers, communications with NOAA, and recommendations for the improvement of outlook products.  In Year 1, UVM will contribute to synthesis of results from Objectives 1-3. In Year 2, UVM will contribute to synthesis of results and development of best practice recommendations from Objectives 1-4. UVM will contribute to the development of at least one article submitted for peer-reviewed publication and at least 1-2 public-facing communications of project results. 
 
Publications, Presentations and Posters

Conference Presentations
Jefferson, A., & Taylor, L.. (2024). Understanding Differences Across Audiences to Improve Flood Communications. UVM CIROH All Hands Meeting 2024; Burlington, VT; https://uvmoffice-my.sharepoint.com/:v:/g/personal/water_uvm_edu/EeuiMi1Ag_dOvon82x0gwKsBP66lqbxgTatLjvdhhBAw_Q?e=HzkTP0

Other Publications
Kandel, S., Jefferson, A., Fedoroff, M., & Kenney, M.A.. (2024). Audience Segmentation to Improve Usability Flood Inundation Mapping: Engagement and Testing with Technical Users and Impacted Communities. . CIROH Working Group 4 Monthly Meeting.

Fedoroff, M., Kandel, S., Jefferson, A., Taylor, L., Kreiter, A., Gerst, M., Sharma, S., Joshi, A., Abshire, Kate., Vallee, D., & Kenney, M.A.. (2024). Collaborative Approaches to Working on Flood Challenges in Indigenous Communities. . National Weather Service Leadership Briefing.

Posters
Jefferson, A. , Stumpf, A., Kenney, M.A., Kandel, S., Taylor, L., Kreiter, A., Sharma, S., Joshi, A.,Fedoroff, M.. (2024). Audience Segmentation to Improve Usability Flood Inundation Mapping: Engagement and Testing with Technical Users and Impacted Communities. UVM CIROH All Hands Meeting 2024; Burlington, VT; https://www.uvm.edu/water/ciroh/2024-ciroh-all-hands-meeting#agenda

Kandel, S., Jefferson, A., Fedoroff, M., Taylor, L., Kreiter, A., Gerst, M., Sharma, S., Joshi, A., Abshire, Kate., Vallee, D., & Kenney, M.A.. (2024). Audience Segmentation to Improve Usability Flood Inundation Mapping: Engagement and Testing with Technical Users and Impacted Communities. CPASW 2024 and CDPW 2024; Tallahassee, FL; https://origin.cpc.ncep.noaa.gov/products/outreach/CDPW/48/programs/cdpw48-cpasw-agenda.pdf

Title: Optimizing Flood Warning Information Sharing for Local Stakeholders through Science Communication Research
 
This project is part of the multi-institutional project:
Optimizing Flood Warning Information Sharing for Local Stakeholders through Science Communication Research 
 
Project Leads:  Dr. Anne Jefferson, Dr. Elizabeth Doran  
 
University of Vermont Research Plan: University of Vermont researchers will collaborate with researchers from RTI International to investigate local stakeholders (e.g., local officials, small business owners, homeowners, community services) as potential end-users for current and future National Water Model (NWM) and flood inundation map (FIM) forecast products. Through mixed-methods research, we will first gain a more complete understanding of existing flood information perceptions and uses across sectors and how they differ by community features (e.g., size, geography). We will then conduct workshops to explore alternative FIM data displays and information communications, gathering data to guide user-centric flood forecast design and dissemination strategies that foster trust and understanding, enabling the public to take effective action during flood events.  
 
Work In Progress: We are focusing on understanding how three types of community organizations use data to inform action, including exploring the role of flood forecast information displays (e.g., FIMS, depth modeling). Our primary audiences include local officials (e.g., utilities, public health departments), small businesses, homeowners, and community services (e.g., hospitals, shelters, food banks), and community interest organizations (e.g., large employers, faith-based organizations).  UVM researchers will conduct focus groups in two northeastern US communities who have experienced flooding in the past ten years to understand how communities use flood information for decision-making and action. Within each community, we will conduct three to four focus groups segmented by the above audiences as relevant and available. Participant recruitment will leverage local connections to emergency managers, health officials, and involved community representatives; engagement with individuals at conferences or relevant meetings; and snowball sampling. Comparing results from the northeastern US communities to focus groups conducted by RTI researchers in other parts of the US will allow UVM and RTI researchers to collaboratively understand how community use of flood information varies with geography and community size. We are supplementing the qualitative data collection with a survey that has wider reach to flood-prone communities around the U.S. to determine what processes and information uses are broadly applicable and which might be unique to a particular region, geography, level of resources, access to technology, or data literacy.  
 
In fall/winter 2024-2025, we will convene design-thinking sessions engaging the community in a dialogue centered around the question of “How are current flood information dissemination practices and communication products applied and how can processes and products be improved to promote understanding and action?” These sessions will include representatives from across audience segments, if feasible in the same communities where we conducted focus groups in Year 1. UVM researchers will continue to recruit and collect data in the northeastern United States, running at least one design-thinking session.  
 
UVM researchers will conduct data analysis and synthesis from focus groups, the survey, and the design-thinking workshops.  We will develop research products (e.g., manuscripts, white papers, conference presentations) for dissemination and will support communication of research findings to public outlets in the communities where we did our work.
 
Publications, Presentations and Posters

Conference Presentations
Jefferson, A., Taylor, L. E., Brown, J., Doran, E., DeBree, S., Johns, C., Luukinen, B., Noyes, S., Southwell, B.. (2024). Flood warning information sharing within community organizations. WSC 2024 Western Snow Conference ; Saint Paul, MN; https://agu.confex.com/agu/hydrology24/meetingapp.cgi/Paper/1502428

Taylor, L. E., Jefferson, A., Brown, J., Doran, E., DeBree, S., Johns, C., Luukinen, B., Noyes, S., Southwell, B.. (2024). Instructional narratives: Flood warning information sharing within community organizations. Natural Hazards Center Researcher’s Meeting; Broomfield, CO; https://hazards.colorado.edu/workshop/2024/abstract/researchers-meeting

Posters
Taylor, L. E., Brown, J. A., Jefferson, A. J., Doran, E., DeBree, S., Johns, C., Luukinen, B., Noyes, S., Southwell, B.. (2024). Analyzing Flood Warning Communication in Local Communities Using the IDEA Model. CIROH Training and Developers Conference 2024; Salt Lake City, UT; https://ciroh.ua.edu/abstracts/analyzing-flood-warning-communication-in-local-communities-using-the-idea-model/

Brown, J. A., Luukinen, B., Johns, C., DeBree, S., van Houtven, G., Southwell, B., van Werkhoven, K., Jefferson, A., Doran, E., Taylor, L. E., Noyes, S.. (2023). Optimizing flood warning information sharing for local stakeholders through science communication research. CIROH 2023 Science Meeting; Tuscaloosa, AL;

Title: Modeling Community Trust: A Collaborative Approach to Scoping Water Forecasting Needs and NOAA Product Use in Indigenous Communities of Northeastern Oklahoma 
 
This project is part of the multi-institutional project:
Modeling Community Trust: A Collaborative Approach to Scoping Water Forecasting Needs and NOAA Product Use in Indigenous Communities of Northeast Oklahoma
 
Project Lead: Dr. Scott Merrill

University of Vermont Research Plan: This project will assist in the needs assessment, scoping and development of dynamic flood inundation and risk simulation models, and serious games to align with the needs of Indigenous communities in northeastern Oklahoma and beyond. Dr. Scott Merrill will serve as the PI for the UVM subaward. Dr. Merrill and Dr. Trisha Shrum (UVM) will work closely with PI Fedoroff, CUASHI researchers (Raub and Laufer), and the University of Kansas (Koliba) to assist with listening sessions, interviews, and empaneled focus groups with tribal leaders, tribal members, tribal hydrologists and planners, and NOAA NWS and RFC leads for the region. Additionally, Merrill will oversee the development of tools that will simulate flood hazard scenarios. Such tools will serve as visualization tools and discussion pieces to promote increased understanding of NOAA products and facilitate communication about the potential needs of the Tribal communities, as well as how to effectively understand and discuss flood hazards. Merrill and Shrum will lead efforts to explore how to effectively collect dynamic decision-making and behavioral data. Merrill will oversee an undergraduate student who will assist in the development of simulation models and serious games.
 
Publications, Presentations and Posters

Conference Presentations
Hale, K., Wemple, B., Shanley, J., Grunes, A., Bomblies, A.. (2023). Characterizing the Vermont Snowpack. ESC-2023 Eastern Snow Conference; Easton, PA; https://static1.squarespace.com/static/58b98f7bd1758e4cc271d365/t/65b12ffa2ee1614019e0d237/1706110970182/61.Hale1_ESC23.pdf

DiPietro, T., Chow, C., Grunes, A., Wemple, B., Hale, K.. (2024). Modeling of Snowpack in The Northeast Using iSnobal. CIROH Training and Developers Conference 2024; Salt Lake City, UT; https://ciroh.ua.edu/abstracts/modeling-of-snowpack-in-the-northeast-using-isnobal/

Posters
DiPietro, T., Hale, K., Wemple, B.. (2024). Advancing CONUS-Scale Operational Snow Modeling Capabilities. UVM CIROH All Hands Meeting 2024; https://www.uvm.edu/ciroh/sje/docs/2024-CIROH-All-Hands/UVM-2024-CIROH-All-Hands-Agenda_09Aug2024.pdf

Chow, C., DiPietro, T., Grunes, A., Wemple, B., Hale, K.. (2024). Modeling of Snowpack in The Northeast Using iSnobal. WSC 2024 Western Snow Conference ; Corvallis, OR ; https://westernsnowconference.org/wp-content/uploads/2024_WSC_DRAFT_AGENDA.pdf

Hale, K., A. Schroth, J. Shanley, B. Wemple. (2023). Warmer, dynamic winters drive declines in snowpack and coupled increases in annual and seasonal runoff in a headwater catchment of northeastern USA. AGU Fall Meeting 2023; San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1347688

 
Title: Advancing Science to Better Characterize Drought and GroundwaterDriven Low-Flow Conditions in NOAA and USGS National-Scale Models
 
This project is part of the multi-institutional project:
Advancing Science to Better Characterize Drought and Groundwater Driven Low-Flow Conditions in NOAA & USGS National-Scale Models 
 
Project Lead: Dr. Donna Rizzo

University of Vermont Research Plan: We propose to develop state-of-the-art methods to improve national-level streamflow forecasts for low-flow conditions, where, in some regions, flows are dominated by baseflow from groundwater discharge rather than runoff from precipitation events. We will train machine learning algorithms using multiple observations including landcover/land-use, rainfall, soil moisture, and groundwater level datasets to identify reaches influenced by groundwater. Once these regions have been identified, we will develop methods to more accurately predict baseflow conditions in neighboring streams. We will refine and augment groundwater data with Earth observations to improve accuracy and compensate for sparse and missing groundwater data. We will leverage work being done with the USGS on Long-Short Term Memory (LSTM) models enhanced with feature selection. We will test Physics Informed Neural Networks (PINN) to ensure that our predictions are reasonable and so that they can be used reliably in sparsely gaged basins. We will develop physical models to calibrate and improve our machine learning algorithms. We will employ our resulting prediction methods to generate forcings and determine how to link these boundary conditions to the NextGen NWM using the HydroFabric framework. This work will improve the National Water Model (NWM) predictions for low-flow conditions, which to date have received less attention due to the emphasis on predicting extreme flooding events. These predictions will support operations where low-flow conditions are critical, including drought management, water supply, minimum flow rates for critical infrastructure, ecological sustainability, and river navigation. We will work with the USGS to ensure algorithms are compatible with computational frameworks/projects and that the USGS can use our results.  
 
Background: In a recent collaboration with U.S. Geological Survey (USGS), the University of Vermont (UVM) has been leveraging the USGS low flow ML modeling efforts being developed within their Water Resources Mission project – Data-Driven Drought Prediction project. Specifically, the USGS has developed and is currently testing deep learning models, known as Long Short-Term Memory (LSTM) models, to improve daily estimates of low streamflow and to forecast streamflow drought at lead times ranging from 0 days to 60 days. This UVM statement of work will leverage these on-going collaborative efforts to assess and improve LSTM model performance with a focus on forecasts for streamflow under drought conditions. In recent decades, the duration and deficit volume of streamflow droughts – defined as abnormally low streamflow and the resulting lack of water in the hydrological system (Van Loon, 2015) – have increased in the southern and western U.S. (Hammond et al., 2022). The proposed ML methods offer an approach to increase the accuracy of the NWM predictions for low-flows (i.e., streamflow drought forecasts) and expand the spatial coverage of these forecasts, which to date have received less attention due to the emphasis on predicting extreme flooding events. Under low-flow conditions, groundwater (GW) contributions to base flow become a critical forcing, and characterizing GW interactions with streamflow at a continental scale is critical.
 
Proposed Training/Testing Data: BYU has recently used ML tools that leverage Earth observational datasets to impute gaps in historical groundwater level records (S. Evans et al., 2020; S. W. Evans et al., 2020; Ramirez et al., 2022). The regional USGS LSTM models are being trained and tested using 40 years (1980-2020) of daily streamflow data from 425 streamgages within the Colorado River Basin and surrounding area. In addition to estimating low streamflows at the gages, now-casting of the latter are being assessed at ungaged locations. The LSTM input features include a large set of static watershed attributes available for the National Hydrography NHDPlus V2.1 catchments (Wieczorek et al., 2018) as well as meteorological and remotely sensed dynamic forcing inputs that have been aggregated to basin averages. Proposed Tasks: UVM will leverage the above big data and existing ML models and, in concert with BYU and UA, will work to improve low streamflow estimates as well as short- and medium-range streamflow drought forecasts. To date, UVM has been performing feature selection to rank the importance of the LSTM input features with respect to model performance accuracies. Both UVM and the USGS will present preliminary findings at the SEDHYD conference in St. Louis, MO in May, 2023. USGS has publicly released daily streamflow streamflow percentiles and drought event datasets for gages spanning all of CONUS (Simeone, 2022) and will be preparing a data release of compiled model input features, so that all data are publicly available. As a result, we expect these data will be available to our BYU-UA-UVM research team as early as late Spring 2023. LSTM model performance is currently being assessed using a variety of performance metrics.  
 
Because BYU has shown that aquifer storage curves correlate closely with long-term baseflow patterns observed in nearby streams and rivers, we hypothesize that LSTM model performance metrics will be correlated to the degree of groundwater-surface (GW-SW) water interactions. Thus, streamflows that correlate well with groundwater levels (e.g., perennial streams) may help improve forecasts of baseflow under low flow conditions. UVM proposes to expand the preliminary feature selection with an iterative clustering approach using new ML clustering tools to assess/improve LSTM model performance. Specifically, we will:  
 
1) Cluster USGS gaged watersheds based on the LSTM model performance metrics and then perform feature selection on a clustered watershed basis. In year one, we will use the upcoming 2023 USGS data release. In year two, after groundwater baseflow data have been compiled by BYU, we will repeat the cluster-feature selection analysis (i.e., re-cluster watersheds based on their degree of GW-SW water interactions and repeat feature selection).  
 
2) First perform a feature importance analysis on a watershed-by-watershed basis using a model input that varies dynamically (e.g., meteorology, degree of GW-SW water interaction, sensitivity to nearby groundwater levels), and then cluster the watersheds based on the importance/strength of the ranked input features. In this manner, we’d be investigating whether the LSTM models are able to capture SW-GW interactions.  
 
3) Tasks 1) and 2) above can be re-done on a seasonal basis to leverage, and perhaps identify, those times of the year when intermittent streams might provide added predictive value.  
 
4) Incorporate one (or more) GW-SW baseflow constraints into the loss/objective component of the USGS LSTM models. If time permits: (i) the USGS LSTM models could be re-trained using the input features selected in tasks above, and/or (ii) we might incorporate the BYU-UA groundwater baseflow data.
 
Publications, Presentations and Posters

Conference Presentations
Norm Jones, Gus Williams, Donna Rizzo, Prabhakar Clement. (2024). Advancing Science to Better Characterize Drought and Groundwater-Driven Low-Flow Conditions in NOAA and USGS National-Scale Models. CIROH Training and Developers Conference 2024; Salt Lake City, UT; https://ciroh.ua.edu/abstracts/advancing-science-to-better-characterize-drought-and-groundwater-driven-low-flow-conditions-in-noaa-and-usgs-national-scale-models/

Norm Jones, Gus William, T. Prabhakar Clement, Donna Rizzo. (2023). Advancing Science to Better Characterize Drought and Groundwater-Driven Low-Flow Conditions in NOAA and USGS National-Scale Models. CIROH Training and Developers Conference 2023; Tuscaloosa, AL;

Posters
Xueyi Li, Amin Aghababaei, Norm Jones, and Gustavious Williams – Brigham Young University; Eniola Webster-Esho, Prabhakar Clement – The University of Alabama; Ryan Van Der Heijden, Donna Rizzo – University of Vermont. (2024). BASEFLOW: A Python package for digital baseflow separation and analysis. CIROH Training and Developers Conference 2024; Salt Lake City, UT; https://ciroh.ua.edu/abstracts/baseflow-a-python-package-for-digital-baseflow-separation-and-analysis/

Eniola Webster-Esho, T. Prabhakar Clement, Xueyi Li, Amin Aghababaei, Gustavious Williams, Norm Jones, Ryan van der Heijden, Donna M. Rizzo. (2024). Gradient-based Method for Automatically Generating Labelled Baseflow Dataset. CIROH Training and Developers Conference 2024; Salt Lake City, UT; https://ciroh.ua.edu/abstracts/gradient-based-method-for-automatically-generating-labelled-baseflow-dataset/

Amin Aghababaei, Xueyi Li, Norm Jones, Gustavious Williams, Eniola Webster-Esho, Prabhakar Clement, Ryan van der Heijden, Donna Rizzo. (2024). Nationwide Identification of Baseflow Dominant Periods: Integrating Manual Expertise into Machine Learning. CIROH Training and Developers Conference 2024; Salt Lake City, UT; https://ciroh.ua.edu/abstracts/nationwide-identification-of-baseflow-dominant-periods-integrating-manual-expertise-into-machine-learning/

Title: Advancing Snow Observation Systems to Improve Operational Streamflow Prediction Capabilities   
 
This project is part of the multi-institutional project:
Advancing Snow Observation Systems to Improve Operational Streamflow Prediction Capabilities  
 
Project Lead: Dr. Christian Skalka 

Research Team: Rachael Chertok, Soheyl Faghir Hagh.

University of Vermont Research Plan:  At UVM our project has two main research goals: (1) to apply modern IoT technologies to improve NRT data reporting capabilities for distributed monitoring in remote settings, and (2) to develop an embedded machine learning-based algorithm using acoustic data for precipitation phase partitioning, to be deployed on low-cost remote platforms. All technologies are being developed on low-cost, low-power, open-source Arduino platforms. 

To accomplish goal (1) we are combining LoRA communications for LAN networking in remote settings with Satellite communications for WAN networking. This approach enables NRT reporting in essentially any remote setting since satellite communications are available anywhere on earth (unlike, e.g., cellular). And the use of LoRA enables efficient LAN communications even in light of resource constraints (e.g., power) in remote settings, supporting sensor distribution and better spatial resolution in data reporting. 

To accomplish goal (2) we are developing a novel machine learning algorithm that uses acoustic data from low-cost microphones for precipitation phase partitioning- that is, automated detection of rain, sleet, hail, or snow. Our algorithm will be embedded on devices in the field (aka “in the edge”). This is a critical enabling technology for our application, since storage or remote communication of high-bandwidth streaming datatypes such as a raw audio waveforms are ill-suited to low-powered embedded systems with typically constrained data logging capacity and/or low-bandwidth NRT reporting capability. Our method can be integrated into low-cost, low-powered embedded platform developed towards goal (1) with high temporal and spatial resolution. Our system will improve operational understanding of events such as rain-on-snow that are critical to monitoring and predicting snow and water dynamics. 
 
Publications, Presentations and Posters

Conference Presentations
Skiles, S.M., Johnson, R., Skalka, C., Horsburgh, J.S. . (2024). Advancing Snow Observation Systems to Improve Operational Streamflow Prediction Capabilities. WEST Colloquium, University of Utah; Salt Lake City, UT;

Skalka, C.. (2025). Using Open-Source Internet of Things Technologies to Improve Environmental Monitoring. UVM CIROH All Hands Meeting 2024; Burlington, VT; https://uvmoffice-my.sharepoint.com/:v:/g/personal/water_uvm_edu/EUpdDN3sPk9IiUatL46Y-t0BVwoCXgprfPeMnjSpTWCPPQ?e=GyuQqA

Posters
Chertok, R., J. Sober, L. Jensen, C. Skalka . (2024). Detection of Rain on Snow Events and Precipitation Phase Partitioning using Environmental Audio Features. UVM CIROH All Hands Meeting 2024; Burlington, VT; https://www.uvm.edu/water/ciroh/2024-ciroh-all-hands-meeting#agenda

Title: 3D Channel Properties for the OWP Hydrofabric
 
Principal Investigator: Dr. Rebecca Diehl 
 
Research Team: Stewart Kabis and David Baude
 
This project is part of the multi-institutional project:
Novel Geospatial Architecture of Channel and Floodplain Morphological Attributes within the OWP Hydrofabrics 

University of Vermont Research Plan: Realistic representation of channel morphology and hydraulic characteristics is critical to accurate hydrological and flood inundation predictions. Emerging datasets and analyses are transforming our ability to estimate these over the United States. Utilizing these advances within large-scale operational hydrological prediction frameworks will require a new paradigm in the geospatial representation of (3D) channel properties. This project is building on ongoing CIROH projects with partners at Utah State University, the University of Iowa, the University of South Carolina, and the University of Alabama, and expertise within and outside CIROH to contribute to a new geospatial framework directly linked to the OWP hydrofabric. The University of Vermont is focused on the attribution of hydraulically-relevant floodplain morphology that may inform improvements to streamflow predictions and flood inundation mapping outputs. Departures between a benchmark dataset of high-resolution topo-bathymetric surfaces, detailed stage-discharge rating curves, and calibrated inundation maps, and the OWP’s HAND-based synthetic rating curves and flood inundation maps, isolates limitations in the current representation of channel properties. The project’s outcomes are expected to transform the geospatial representation of river networks with direct and rapid translation to the NWC operational architecture and the broader hydrological community.
 
Publications, Presentations and Posters

Posters
Kabis, S., Lawson, S., Underwood, K., Diehl, R.. (2024). Using a Self-Organizing Map to Analyze Hydraulic Features of Vermont Streams to Gain Insight into Flood Response. Geological Society of America 2024 Annual Meeting; Manchester, NH; https://gsa.confex.com/gsa/2024NE/webprogram/Paper397689.html

Title: Enhanced Forecast Design Through Experimental Gaming and Social Impact Assessment of Connected River and Floodplains

Principal Investigator: Scott Merrill

Research Team: Christopher Koliba, Trisha Shrum, Beverley Wemple, Brendan Fisher, Taylor Ricketts

Institution(s): University of Vermont, University of Kansas

Abstract: Extreme water hazard events are increasing in magnitude and frequency, putting individuals, communities and emergency responders at risk, and forcing people to quickly decide how to respond, what information to use and communicate. Our overarching goal is to understand human behavioral aspects associated with current NOAA and National Water Model products that are related to flood water hazards. Our objectives are to better understand flood hazard mitigation and response behaviors to allow for optimization of communication strategies that will allow for more resilient communities, build trust in NOAA products, and help protect infrastructure and lives. Goal 1: Broaden capacity for forecast design by improving the ability of first responders to understand how forecasts will be understood and used by individuals and communities and by providing insights to inform longer-term community planning. The pursuit of this goal provides a significant contribution to CIROH’s efforts to advance social, economic, and behavioral science to improve water prediction and forecast uses. Goal 2: Contribute to the development of the CIROH Enhanced Forecast Design Center by evolving forecast platforms that consider the heterogeneity of risk perception and behavior, and decision heuristics relative to water hazard mitigation that may be integrated into National Water Model products. Objective 1: Gauge public awareness and uses of water hazard forecasting products, including factors impacting variability of risk perception and efficacy of risk communication. Objective 2: Create a platform to test changes to risk perception and communication resulting from alternative forecast design parameters and treatments through a modular experimental gaming platform. Objective 3: Improve integration between stakeholder input and forecast design using representative regional empaneled focus groups. Objective 4: Integrate river and floodplain connectivity to better estimate social impacts and costs using an ecosystem services framework. Accomplishing these goals and objectives will provide actionable, impact-based decision support to NOAA and CIROH scientists that will enhance our ability to develop effective communication strategies, enhance information transfer and understand barriers and opportunities associated with use of National Water Model products.

Publications, Presentations and Posters

Journal Articles
Quainoo, R., Merrill, S.C., Soares, R., Myers, M., Ali-Khan, M., Shrum, T., & Balerna, J.. A National Survey of Flood Hazard Crisis and Risk Perceptions in the United States. Proceedings of the International Crisis and Risk Communication Conference. 2024; doi: 10.69931/UBBE7512

Merrill, S.C., Christopher Koliba, Rodrigo Soares, Ruth Quainoo, Eric Clark, Trisha Shrum, Masood Ali-Khan, Molly Myers & Asim Zia. Simulation-Based Experiments Reveal Differences in the Efficiency of Responses to Flood Warning Messages in Crisis. Proceedings of the International Crisis and Risk Communication Conference. 2024; doi: 10.69931/SMHU9552

Soares, R., Koliba, C., Merrill, S.C., Shrum, T., Quainoo, R., Myers, M., Ali-Khan, M., Balerna, J.A., & Spett, E.. Stakeholders' perceptions of the 2023 historic floods in Vermont. Risk Communication, Crisis Response, Vulnerability and Lessons Learned. Proceedings of the International Crisis and Risk Communication Conference. 2024; doi: 10.69931/AYIR6279

Soares, Rodrigo and A. Balerna, Jessica and Quainoo, Ruth and C. Merrill, Scott and Ali-Khan, Masood and Shrum, Trisha R. and Koliba, Christopher J. “The Great Vermont Floods of 2023”: A Risk and Crisis Communication Assessment. Society & Natural Resources. 2025; doi:10.1080/08941920.2025.2553353

Title: Leveraging emerging sensing technology and machine learning to improve and expand hydrological forecasting to hyper-local scales with NWM-coupled adaptive sensor networks

Principal Investigator: Andrew Schroth

Research Team: Mirce Morales, Beverley Wemple, Jamie Shanley, Jarlath Oneil-Dunne

Institution(s): University of Vermont, USGS

Abstract: Much of the northeastern U.S. is dominated by montane headwater catchments, and recent flooding events illustrate the need for accurate and timely forecasts for such systems. However, model forecast accuracy is often reduced in mountainous regions due to sparse gaging, complex topography, and spatially heterogeneous rainfall/runoff patterns. This project aims to improve the performance and expand the capacity of the National Water Model (NWM) forecasting in montane headwater catchments by achieving three main objectives: 1) assess the NWM performance in montane headwaters, which will improve our understanding of the combinations of geophysical and hydro-climatic forcings that govern streamflow at the subwatershed scale during different events and across seasons, and develop a machine learning correction algorithm that improves flow forecasts in these systems that could be operationalized, 2) deploy a distributed water level sensing network in focal Vermont watersheds in different river corridor environments upstream and downstream of gages that will allow us to use the NWM to forecast water level across different reach environments, and 3) predict water levels at different locations within each focal watershed based on the high-frequency water level data and NWM flow forecasts using machine learning. Upon completion of this project, users and managers of streams in montane watersheds will be able to easily access hyper-localized water level forecasts based on short-range NWM discharge predictions that are post-processed via adaptive sensing and machine learning models. Ultimately, we intend to develop an operational workflow that would allow other communities across the country to improve the performance of these forecasts and leverage relatively low-cost sensor technologies to provide distributed NWM-derived water level forecasts across environments and infrastructures of concern. Improving (through correction algorithms) and expanding (through distributed water level forecasting) the forecast capacity at these sites and providing a template for others to do so will improve operational workflows and extend water resources predictions, capabilities, and applications. Furthermore, the approaches developed here will be particularly suitable for the modular and model-agnostic environment envisioned for the NWM Next Generation Water Resources Modeling Framework (NextGen).

Publications, Presentations and Posters

Conference Presentations
Kemper, J.T., Underwood, K.L., Hamshaw, S.D., Schroth, A.W.. (2024). Coupling data-driven models to streamflow predictions to forecast water quality in sensitive watersheds. American Geophysical Union Water Science Conference; Saint Paul, MN; https://agu.confex.com/agu/hydrology24/meetingapp.cgi/Paper/1502878

Kemper, J.T., Underwood, K.L., Schroth, A.W.. (2024). Coupling Data-driven Models to Streamflow Predictions to Forecast Water Quality in Sensitive Watersheds. 12th International Congress on Environmental Modelling and Software (iEMSs); Saint Paul, MN;

Dehabadi, M. Schroth, A.W.,, Schneebeli, S.Badireddy, A.R.. (2024). Designing low cost novel nutrient sensors for distributed network water quality monitoring and forecasting. UVM CIROH All Hands Meeting; Burlington, VT; https://uvmoffice-my.sharepoint.com/:v:/g/personal/water_uvm_edu/Eb6_qOu7EE1MjSMjTn-m0PYB21XOZmevWWiCtZcSWv0Fmg?e=b7xgzD

Kemper, J.T. Underwood, K., Hamshaw, S. Shanley, J. B. Wemple, B., O’Neil-Dunne, J., Schroth, A.W.. (2023). Leveraging geospatial datasets to inform development of water quality and quantity monitoring networks that can improve forecasting capacity in watersheds. American Water Resource Association Summer Meeting; Denver, CO.;

Schroth, A.W., Morales, M., Kemper J.T.. (2023). Leveraging sensor technology to improve and expand National Water Model forecast capacity. UVM CIROH All Hands Meeting; Burlington, VT; https://uvmoffice-my.sharepoint.com/:v:/g/personal/water_uvm_edu/Efxm1oKgej1Ans0UTB18hkoBPPa7qgTCphuMWPjKe560Yg?e=7HtfUL

Badireddy, A.R., Worley, R. Wyatt, M., Seeberger, K.. (2022). Nano-enhanced Potentiometric Sensors for Improving Soil Health. Health Sustainable Nanotechnology Conference Program; Austin, TX;

Kemper, J.T., Underwood, K.L., Schroth, A.W.. (2024). Post-processing National Water Model output to forecast water quality for management applications in diverse watersheds. 12th International Congress on Environmental Modelling and Software (iEMSs); East Lansing, MI;

Schroth, A.W., Adair, C., Bowde,W.B3, Blocher,S, Serchan, S., Kemper, J.T., Underwood, K. Vaughan, M., Kinkaid, D.W., Seybold, E.C., Perdrial, J.N., Vogel, S., Duffy. (2024). The Northeastern Water Resources Monitoring Network-Vermont Edition. American Geophysical Union Water Science Conference; Saint Paul, MN; https://agu.confex.com/agu/hydrology24/meetingapp.cgi/Paper/1502799

Posters
Dehabadi, M., Glazer, J., Schroth, A.W., Badireddy, A.R.. (2024). Development of Phosphate-Selective Sensors Based on Macrocyclic Ionophore-Doped Membranes. American Institute of Chemical Engineers (AIChE) annual meeting; San Diego, CA;

Kemper, J.T. Underwood, K., Hamshaw, S. Shanley, J. B. Wemple, B., O’Neil-Dunne, J., Schroth, A.W. (2023). Leveraging geospatial datasets to inform development of water quality and quantity monitoring networks that can improve forecasting capacity in watersheds. American Water Resource Association Summer Meeting; Denver, CO;

Kemper, JT, Underwood, K. Hamshaw, S. Shanley, J.B., Schroth, A.W.. (2023). Leveraging High Resolution Sensor Data and Large-Scale Physical Models to Monitor and Forecast Critical Water Quality Parameters in Sensitive Watersheds. AGU Fall Meeting 2023; San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1340045

Title: Coupling novel low cost spatially distributed nutrient sensors and National Water Model output to forecast nutrient loading and inform state implementation of EPA mandated nutrient reduction targets—The Lake Champlain Basin Test Bed
 
Principal Investigator: Andrew Schroth

Research Team: John Kemper, Kristen Underwood, Scott Hamshaw, Jamie Shanley, Raju Badireddy, Monireh Dehabadi, Severin Schneebelli, Jarlath O’Neil-Dunne

Institution(s): University of Vermont, USGS, Purdue University 

Abstract: While not included in the current version of the National Water Model (NWM), there is vast potential and associated demand to expand the model’s capacity to forecast water quality. Here, we are focused on leveraging NWM flow forecasts to force novel nutrient loading forecasts for select basins within the Lake Champlain Basin. This is a particularly relevant use case, as the Lake Champlain Basin has been mandated to reduce phosphorus loading through the Total Maximum Daily Load (TMDL) framework to improve impaired waters as mandated by Section 303a of the Clean Water Act. To develop these forecasts, we will primarily utilize long-term and high frequency concurrent flow and phosphorus concentration time series and machine learning algorithms to develop robust predictive models of nutrient loading.  Initial studies will focus on developing models in two small watersheds of distinct landcover monitored with sensors by our group since 2014 that have high frequency observational time series (nutrient concentration and flow measurements taken every 15 minutes). We will then expand our models to larger watershed systems within the basin that have flow measurements by USGS gages and long-term phosphorus monitoring by the Vermont Department of Environmental Conservation (grab water samples going back to 1991). These will constitute the first NWM-forced nutrient loading models. As loading model development is ongoing, we will also develop low-cost electrochemical phosphate sensors to be distributed spatially within our focal watershed across different phosphate source environments. Ultimately, we plan to use these distributed sensor data with NWM forcing and machine learning to forecast not only the riverine load of phosphate in test case watersheds during storms, but also the source environments of the phosphate. Beyond research papers and presentations, one of the most significant outcomes of this work will be providing a template for others to use the NWM with water quality monitoring data to produce water quality forecasts across the country. We intend to develop an approach that will be fully compatible with NWM Nextgen that is also NWM version agnostic, thus being a useful long-term Research to Operations tool that expands the operations context and community. Given that there are over 50,000 impaired waters governed by TMDLs in the United States, the potential for this research to expand the operations community and NWM utility is substantial. 
 
Publications, Presentations and Posters

Conference Presentations
Kemper, J.T., Underwood, K.L., Hamshaw, S.D., Schroth, A.W.. (2024). Coupling data-driven models to streamflow predictions to forecast water quality in sensitive watersheds. American Geophysical Union Water Science Conference; Saint Paul, MN; https://agu.confex.com/agu/hydrology24/meetingapp.cgi/Paper/1502878

Kemper, J.T., Underwood, K.L., Schroth, A.W.. (2024). Coupling Data-driven Models to Streamflow Predictions to Forecast Water Quality in Sensitive Watersheds. 12th International Congress on Environmental Modelling and Software (iEMSs); Saint Paul, MN;

Dehabadi, M. Schroth, A.W.,, Schneebeli, S.Badireddy, A.R.. (2024). Designing low cost novel nutrient sensors for distributed network water quality monitoring and forecasting. UVM CIROH All Hands Meeting; Burlington, VT; https://uvmoffice-my.sharepoint.com/:v:/g/personal/water_uvm_edu/Eb6_qOu7EE1MjSMjTn-m0PYB21XOZmevWWiCtZcSWv0Fmg?e=b7xgzD

Kemper, J.T. Underwood, K., Hamshaw, S. Shanley, J. B. Wemple, B., O’Neil-Dunne, J., Schroth, A.W.. (2023). Leveraging geospatial datasets to inform development of water quality and quantity monitoring networks that can improve forecasting capacity in watersheds. American Water Resource Association Summer Meeting; Denver, CO.;

Schroth, A.W., Morales, M., Kemper J.T.. (2023). Leveraging sensor technology to improve and expand National Water Model forecast capacity. UVM CIROH All Hands Meeting; Burlington, VT; https://uvmoffice-my.sharepoint.com/:v:/g/personal/water_uvm_edu/Efxm1oKgej1Ans0UTB18hkoBPPa7qgTCphuMWPjKe560Yg?e=7HtfUL

Badireddy, A.R., Worley, R. Wyatt, M., Seeberger, K.. (2022). Nano-enhanced Potentiometric Sensors for Improving Soil Health. Health Sustainable Nanotechnology Conference Program; Austin, TX;

Kemper, J.T., Underwood, K.L., Schroth, A.W.. (2024). Post-processing National Water Model output to forecast water quality for management applications in diverse watersheds. 12th International Congress on Environmental Modelling and Software (iEMSs); East Lansing, MI;

Schroth, A.W., Adair, C., Bowde,W.B3, Blocher,S, Serchan, S., Kemper, J.T., Underwood, K. Vaughan, M., Kinkaid, D.W., Seybold, E.C., Perdrial, J.N., Vogel, S., Duffy. (2024). The Northeastern Water Resources Monitoring Network-Vermont Edition. American Geophysical Union Water Science Conference; Saint Paul, MN; https://agu.confex.com/agu/hydrology24/meetingapp.cgi/Paper/1502799

Posters
Dehabadi, M., Glazer, J., Schroth, A.W., Badireddy, A.R.. (2024). Development of Phosphate-Selective Sensors Based on Macrocyclic Ionophore-Doped Membranes. American Institute of Chemical Engineers (AIChE) annual meeting; San Diego, CA;

Kemper, J.T. Underwood, K., Hamshaw, S. Shanley, J. B. Wemple, B., O’Neil-Dunne, J., Schroth, A.W.. (2023). Leveraging geospatial datasets to inform development of water quality and quantity monitoring networks that can improve forecasting capacity in watersheds. American Water Resource Association Summer Meeting; Denver, CO;

Kemper, JT, Underwood, K. Hamshaw, S. Shanley, J.B., Schroth, A.W.. (2023). Leveraging High Resolution Sensor Data and Large-Scale Physical Models to Monitor and Forecast Critical Water Quality Parameters in Sensitive Watersheds. AGU Fall Meeting 2023; San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1340045

Title: Post-processing NWM output with spatially distributed turbidity sensing to forecast turbidity loading and source for reservoir operation management

Principal Investigator: Andrew Schroth

Research Team: John T. Kemper, Kristen L. Underwood, Jarlath O’Neil-Dunne, Scott Hamshaw, Jamie Shanley 

Institution(s): University of Vermont, USGS 

Abstract: Reservoir operations and management increasingly extend beyond considerations of water volume and water level to include water quality, especially in situations where reservoir water is unfiltered or treatment of a particular contaminant is onerous. A primary concern regarding reservoir water quality is often turbidity – a measure of water clarity that is chiefly impacted by how much sediment and other material is suspended in the water column – which can impede reservoir operations when certain levels are exceeded. Because the National Water Model (NWM) has been shown to have substantial utility for reservoir operations by providing flow forecasts that inform anticipation of future water volumes, it is sensible to leverage this forecasting capability to provide insight into future turbidity levels. Additionally, many prior studies have suggested that turbidity is primarily influenced by water discharge and other hydrologic parameters forecasted by the NWM (such as rainfall) as well as easily obtainable watershed characteristics (such as geology), indicating turbidity prediction may be readily achievable by coupling the hydrologic forecasting capability of the NWM to empirical models of turbidity production. In this project, we employ such an approach in the Esopus Creek catchment in the Catskills Mountains of New York State, which drains to the Ashokan Reservoir of New York City water supply system and has been extensively monitored for the past decade. We build off prior research to construct a machine learning-based model of turbidity as a function of antecedent conditions, storm hydrology, and watershed characteristics. In initial testing, this model, which leverages the distributed, high-resolution sensor network present in the Esopus watershed, outperforms the current model used by the New York City Department of Environmental Protection (NYC DEP) in reservoir operations. These preliminary results support the utility of our proposed approach and suggest that machine-learning models built on understanding of watershed processes can be fed NWM forecast products to successfully anticipate future turbidity loading. Work in the second and third year of the project will continue to fine-tune turbidity models with additional site-specific data from the Esopus sensor network to further improve forecasting capabilities and enhance forward-thinking reservoir operations. In particular, the next steps will aim to forecast not only turbidity levels, but also anticipate where in the watershed turbidity will be produced. This type of source-specific forecasting has substantial importance for both operational planning and management efforts (e.g., erosion mitigation) to suppress sediment loading to Esopus streams. Overall, results of this project will emphasize the capabilities of the NWM to extend beyond hydrologic forecasts and provide a blueprint for others interested in leveraging such abilities to produce water quality predictions. 
 
Publications, Presentations and Posters

Conference Presentations
Kemper, J.T.. (2023). Exploring the multifaceted impacts of increased sediment supply on fluvial system form and function. Geological Society of America 2023 Annual Meeting; Pittsburgh, PA; https://gsa.confex.com/gsa/2023AM/meetingapp.cgi/Paper/391375

Swami, S., Underwood, K.L., Hamshaw, S.D., Wshah, S., Davis, D., Rizzo, D.M.. (2023). Forecasting River Turbidity using Innovative Machine Learning Techniques. SEDHYD – Sedimentation & Hydrologic Modeling Conference; St. Louis, MO; https://www.sedhyd.org/2023Program/1/318.pdf

John T. Kemper, Kristen L. Underwood, Andrew W. Schroth – University of Vermont; Scott D. Hamshaw, James B. Shanley – U.S. Geological Survey. (2024). Forecasting water quality from National Water Model outputs at actionable scales. CIROH Annual Developers Conference; Salt Lake City, UT;

Kemper, J.T. Underwood, K., Hamshaw, S. Shanley, J. B. Wemple, B., O’Neil-Dunne, J., Schroth, A.W.. (2023). Leveraging geospatial datasets to inform development of water quality and quantity monitoring networks that can improve forecasting capacity in watersheds. American Water Resource Association Summer Meeting; Denver, CO.;

Schroth, A.W., Morales, M., Kemper J.T.. (2023). Leveraging sensor technology to improve and expand National Water Model forecast capacity. UVM CIROH All Hands Meeting; Burlington, VT; https://uvmoffice-my.sharepoint.com/:v:/g/personal/water_uvm_edu/Efxm1oKgej1Ans0UTB18hkoBPPa7qgTCphuMWPjKe560Yg?e=7HtfUL

Swami, S., Underwood, K.L. , Rizzo, D.M.. (2024). Optimizing RNN Architectures for Improved Turbidity Predictions: Exploring the Impact/Potential of Fine-Tuning in Hydrological Forecasting. 12th International Congress on Environmental Modelling and Software (iEMSs); East Lansing, MI;

Kemper, J.T., Underwood, K.L., Schroth, A.W.. (2024). Post-processing National Water Model output to forecast water quality for management applications in diverse watersheds. 12th International Congress on Environmental Modelling and Software (iEMSs); East Lansing, MI;

Other Publications
Schroth, A.W., Underwood, K.L. Kemper, J.T., Hamshaw, S.D.. (2024). Exploring synergies with UVM and USGS turbidity research in the Ashokan Reservoir’s watershed. . Online meeting with team presentations designed to explore synergies.

Schroth A.W. and Underwood, K.L.. (2022). Introduction to UVM CIROH efforts in the Ashokan Reservoir Watershed. . Online meeting: Introduction of CIROH research efforts in the region and discuss synergies.

Posters
Kemper, JT, Underwood, K. Hamshaw, S. Shanley, J.B., Schroth, A.W.. (2023). Leveraging High Resolution Sensor Data and Large-Scale Physical Models to Monitor and Forecast Critical Water Quality Parameters in Sensitive Watersheds. AGU Fall Meeting 2023; San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1340045

Title: Improved Representation of Floodplains and Natural Features for Channel Routing 
 
Principal Investigator: Beverley Wemple 

Research Team: Rebecca Diehl, Kristen Underwood, Julianne Scamardo, Eric Roy, Kenneth Johnson 

Institution(s): University of Vermont 

Abstract: Floodplains play an important role in the attenuation of floods, influencing river forecasts and flood inundation predictions, but they are poorly represented in the National Water Model (NWM). This project aims to improve our understanding and modeled representation of the influence of floodplain-channel connectivity on flood celerity and flood routing processes. Our initial use case is situated in the Northeastern US, comprising over 3000 river reaches in the Vermont portion of the Lake Champlain basin. We use high-resolution topographic data to develop river reach morphological signatures in cross section to characterize floodplain types. A supervised machine-learning algorithm was used to cluster reaches based on their hypothesized influences on flood attenuation. The workflow for topographic signature extraction is publicly available on a GitHub repository, with future improvements planned as we integrate planform complexity into our characterization. Future work in project years 2-3 will involve development and testing of hydrodynamic models to validate hypothesized differences in routing for distinct floodplain classes, and for river reaches in our northeast testbed sites, instrumented with water level and inundation tracking sensors. As an outcome of the hydrodynamic modeling, we aim to identify the reach types for which the simpler representation of flood wave routing (i.e., Muskingum Cunge) is appropriate and which settings may require a more computationally expensive approach to optimize efficiency for enhanced performance of river stage forecasts and inundation extent predictions. Products developed through this project will be helpful for the National Water Center (NWC) and its partners (e.g., USGS) in the development of the Next Generation Water Resources Modeling Framework (NextGen) and geospatial data that support national hydrologic modeling applications.
 
Publications, Presentations and Posters

Journal Articles
Diehl, Rebecca M. and Underwood, Kristen L. and Watt, Robert and Hamshaw, Scott D. and Pahlevan, Nima. Evaluating opportunities for broad-scale remote sensing of total suspended solids on small rivers. Remote Sensing Applications: Society and Environment. 2024; doi: 10.1016/j.rsase.2024.101234

Matt, Jeremy E. and Underwood, Kristen L. and Diehl, Rebecca M. and Lawson, K. S. and Worley, Lindsay C. and Rizzo, Donna M.. Terrain‐derived measures for basin conservation and restoration planning. River Research and Applications. 2023; doi: 10.1002/rra.4181

Conference Presentations
Lawson, K.S., K.L. Underwood, R.M. Diehl, D.M. Rizzo. (2023). Characterizing Duration and Frequency of Flood Events Across Geomorphic Settings. SEDHYD 2023; St. Louis, MO; https://www.sedhyd.org/2023Program/1/296.pdf

Diehl, R.M., S. Lawson, K. Underwood, J. Scamardo, B. Wemple. (2023). Improved Representation of Reach-Scale Floodplain Topography for Floodwater Routing in Large-Scale Models. AGU Fall Meeting 2023; San Francisco, CA; https://ui.adsabs.harvard.edu/abs/2023AGUFM.H34F..02D/abstract

Posters
Lawson, K.S., K.L. Underwood, R.M. Diehl, D.M. Rizzo. (2022). Flow-Duration-Frequency Analysis for the State of Vermont. AGU Fall Meeting 2022; Chicago, IL; https://ui.adsabs.harvard.edu/abs/2022AGUFM.H35I1230L/abstract

Lawson, K.S., R.M. Diehl, K. Underwood, J. Scamardo, B. Wemple. (2023). Functional Floodplain Classes Emerge from Regional Dataset of Hydraulically-Relevant Topographic Features. AGU Fall Meeting 2023; San Francisco, CA; https://ui.adsabs.harvard.edu/abs/2023AGUFMEP51C1628L/abstract

Juli Scamardo, Scott Lawson, Rebecca Diehl, Kristen Underwood, Beverley Wemple. (2024). Incorporating Floodplain Topographic Features to Improve Channel Routing. CIROH Training and Developers Conference 2024; Tuscaloosa, AL; https://ciroh.ua.edu/abstracts/incorporating-floodplain-topographic-features-to-improve-channel-routing/

Scamardo, J., K.S. Lawson, R.M. Diehl, K. Underwood, K. Johnston, B. Wemple. (2023). Investigating the Impact of Floodplain Topography on Flood Attenuation in Low-Order Catchments. AGU Fall Meeting 2023; San Francisco, CA; https://ui.adsabs.harvard.edu/abs/2023AGUFMEP51C1627S/abstract

Title: Forecasting the Incidence and Duration of Harmful Algal Blooms (HABs) at Daily, Weekly and Seasonal Scales 
 
Principal Investigator: Asim Zia

Research Team: Patrick J. Clemins, Panagiotis Oikonomou, Donna Rizzo, Andrew W. Schroth, Safwan Wshah, Peter Isles,Imad Hanoun, Kareem Hanoun, Scott Turnbull, Noah B. Beckage, Mohammad Adil, Montana Bailey, Hakan Unveren, Saul Blocher, Jarlath O’Neil Dunne, Luis D. Espinosa, George Pinder

Institution(s): Department of Community Development and Applied Economics, University of Vermont, Department of Computer Science, University of Vermont, Department of Civil and Environmental Engineering, University of Vermont, Department of Geography and Geosciences, University of Vermont, Department of Environmental Conservation, Vermont Agency of Natural Resources, Water Quality Solutions, Inc., Vermont EPSCOR, University of Vermont, Rubsenstein School of Environment and Natural Resources, University of Vermont, Department of Electrical and Biomedical Engineering, University of Vermont

Abstract: Despite significant advancements in satellite monitoring of Harmful Algal Blooms (HABs), the “accurate” forecasting of HABs and development of “real-time” HABs Early Warning Systems (HABEWS) at finer spatial (200 m to 500 m) and temporal (daily to seasonal) scales still requires a considerable amount of basic and applied research. In this sub-project, we will advance the National Water Model (NWM’s) predictive intelligence for early warnings of HABs at daily, weekly, and seasonal lead time scales by applying machine learning, process-based modeling, and hybrid frameworks that couple the two. This project will scale and test an approach to forecasting HABs in freshwater lakes and estuaries that leverages hydrological forecasts derived from NWM and existing Earth Observation datasets currently being produced in real time through satellites and in situ monitoring systems and sensors. In year 1, WRF-Hydro derived NWM hydrological hindcasts and forecasts will be embedded in an existing Integrated Assessment Model (IAM) computational workflow to drive an already calibrated and validated process-based lake model (AEM3D). The IAM also uses weather data that can be derived from National Weather Model and/or IBM weather forecast products. This workflow will produce high resolution HAB hindcasts and forecasts in two bays of Lake Champlain (Missisquoi Bay and St. Albans Bay). In year 2, we will develop and test a self-learning AI-HABEWS by identifying a best-fitting deep neural network to update AEM3D by validating HAB forecasts through community science monitors, and in situ & satellite sensors. In year 3, we will conduct a sensitivity analysis of HAB forecast accuracy (as predicted by machine-learning, process based, and hybrid forecast models) to hydrological forecasts based on NWM (both WRF-Hydro and Topmodel/NextGen versions), SWAT, and RHESSys models. This sub-project will advance NOAA’s mission to understand and predict the effects of changing climate, weather, and socio-environmental factors on marine ecosystems. This may, in turn, help conserve marine ecosystems and further advance NOAA’s vision of building resilient and healthy ecosystems. 
 
Publications, Presentations and Posters

Journal Articles
Feng, Qingyu and Chen, Liding and Yang, Lei and Yen, Haw and Wang, Ruoyu and Wu, Feng and Feng, Yang and Raj, Cibin and Engel, Bernard A. and Omani, Nina and , Oikonomou, P.D. (0000-0001-6612-0994), & Zia, A. (0000-0001-8372-6090). A distributed model parameter optimization toolbox performing multisite calibration in the lump and distributed mode for the SWAT model. Environmental Modelling & Software. 2023; doi: 10.1016/j.envsoft.2023.105785

Zhang, Xiaohan and Li, Xingyu and Sultani, Waqas and Zhou, Yi and Wshah, Safwan. Cross-View Geo-Localization via Learning Disentangled Geometric Layout Correspondence. Proceedings of the AAAI Conference on Artificial Intelligence. 2023; doi: 10.1609/aaai.v37i3.25457

Zhang, Xiaohan and Li, Xingyu and Sultani, Waqas and Chen, Chen and Wshah, S. (0000-0001-5051-7719). GeoDTR+: Toward Generic Cross-View Geolocalization via Geometric Disentanglement. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2024; doi: 10.1109/tpami.2024.3443652

Book Chapter
Zia, A. (0000-0001-8372-6090). (2024). Towards the Deployment of Food, Energy and Water Security Early Warning Systems as Convergent Technologies for Building Climate Resilience. PP. 99-118. The Water, Energy, and Food Security Nexus in Asia and the Pacific. ISBN 978-92-3-100634-0. doi: 10.1007/978-3-031-29035-0

Conference Presentations
Zia, A., Schroth, A.W., Clemins, P. J. and Oikonomou, P.. (2022). Accounting for Lags, Phase Transitions and Cross Scale Dynamics in Sustaining Freshwater Lakes. 13th Annual Meeting of Earth System Governance; 13th Annual Meeting of Earth System Governance;

Oikonomou, P.D. (0000-0001-6612-0994), Zia, A. (0000-0001-8372-6090), Clemins, P.J. (0000-0002-7930-3025), Turnbull, S. (0000-0002-4384-652X), Schroth, A.W. (0000-0001-5553-3208), Hannoun, K.I., Hannoun, I.A., Isles, P.D.F. (0000-0003-4446-6788), & Rizzo, D.M. (0000-0003-4123-5028). (2023). An Integrated Process-based Modelling Approach for Forecasting Lake Cyanobacteria Blooms Development: A Hindcast Experiment.. American Geophysical Union Fall Meeting 2023 (AGU23); San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1450905

Clemins, P.J. (0000-0002-7930-3025), Beckage, N.B. (0009-0000-9026-9510), Oikonomou, P.D. (0000-0001-6612-0994), Turnbull, S. (0000-0002-4384-652X), & Zia, A. (0000-0001-8372-6090). (2024). Computational Workflow Design for a Cyanobacterial Harmful Algal Bloom (CyanoHAB) Forecast Skill Elasticity Experiment. CIROH Training and Developers Conference 2024; Salt Lake City, UT; https://ciroh.ua.edu/abstracts/computational-workflow-design-for-a-cyanobacterial-harmful-algal-bloom-cyanohab-forecast-skill-elasticity-experiment/

Zia, A.. (2023). Designing and Testing AI augmented Harmful Algal Bloom (HAB) Early Warning Early Action Systems (AI-HABEWS). NOAA Water Node Meeting;

Oikonomou, P.D. (0000-0001-6612-0994), Yen, H. (0000-0002-5509-8792), Clemins, P.J. (0000-0002-7930-3025), Rizzo, D.M. (0000-0003-4123-5028), Schroth, A.W. (0000-0001-5553-3208), Turnbull, S. (0000-0002-4384-652X), & Zia, A. (0000-0001-8372-6090). (2023). Future Climate Impacts on a Highly Heterogeneous Watershed in Vermont. American Geophysical Union Fall Meeting 2023 (AGU23); San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1441066

Zia, A. (0000-0001-8372-6090). (2024). Harnessing Artificial Intelligence augmented Food, Energy and Water Security Early Warning Systems as Convergent Technologies for Building Peace and Climate Resilience. Third International Conference on Environmental Peacebuilding; The Hague, Netherlands;

Zia, A. (2022). Highlands to Oceans (H2O): Anticipatory Governance of Hydroclimatic Regime Shifts in the Transboundary River Basins. UN/WMO/Egyptian Presidency Workshop on Hydrometeorological Early Warning Early Action Systems; Cairo, Egypt;

Zia, A. (2022). Highlands to Oceans (H2O): Piloting AI augmented Hydro-climatic Early Warning Early Action Lead Systems in Transboundary River Basins. UN Climate Conference, COP27; Sharm El Sheikh, Egypt;

Zia, A.. (2023). Highlands to Oceans (H2O): Piloting AI augmented Multi-hazard Early-warning Early Action Lead Systems (AI-MEALS) in Transboundary River Basin. UN Water Conference 2023; UN Headquarters, New York;

Zia, A. (0000-0001-8372-6090), Schroth, A.W. (0000-0001-5553-3208), Isles, P.D.F. (0000-0003-4446-6788), Clemins, P.J. (0000-0002-7930-3025), Oikonomou, P.D. (0000-0001-6612-0994), Turnbull, S. (0000-0002-4384-652X), Beckage, B., Winter, J., & Rizzo, D.M. (0000-0003-4123-5028). (2024). Integrated Harmful Algal Bloom Early Warning Systems Can Quantify the Impact of Early vs. Delayed Policy Actions for Building Climate Resilience. 12th International Congress on Environmental Modelling and Software (iEMSs); East Lansing, MI; https://conference.iemss.org/timetable/event/c7/

Andrew, K, Zia, A., Rizzo, D. (2024). Integrating Deep Reinforcement Learning into Agent-Based Models for Predicting Farmer Adaptation Under Policy and Environmental Variability. Intelligent Systems and Applications: Proceedings of the 2024 Intelligent Systems Conference (IntelliSys), Lecture Notes in Networks and Systems (LNNS); http://dx.doi.org/10.1007/978-3-031-66428-1_13

Zia, A. (0000-0001-8372-6090). (2024). Modeling the Dynamics of Heterogeneous Climate Change Risk Perceptions: An Agent Based Model of US Population, 2010-2030. Conference on Complex Systems (CCS’24),; Exeter, UK;

Oikonomou, P.D. (0000-0001-6612-0994), Clemins, P.J. (0000-0002-7930-3025), Beckage, N.B. (0009-0000-9026-9510), Turnbull, S. (0000-0002-4384-652X), Schroth, A.W. (0000-0001-5553-3208), Rizzo, D.M. (0000-0003-4123-5028), & Zia, A. (0000-0001-8372-6090). (2024). Multi-Scale Forecast Skill Evaluation Framework for Integrated Early Warning Systems. 12th International Congress on Environmental Modelling and Software. 12th International Congress on Environmental Modelling and Software (iEMSs); East Lansing, MI; https://conference.iemss.org/timetable/event/d3/

Zia, A., Schroth, A.W, Clemins, P. J., Oikonomou, P. , Hecht, J., Turnbull, S., Beckage, B.,, Winter, J., Rizzo, D.. (2022). Simulating Lags, Tipping Points and Cross Scale Interactions in Integrated Socio-Environmental Systems: Evaluating the Impacts of Early vs. Delayed Nutrient Reductions under Alternate Hydro-Climatic Scenarios in Missisquoi Bay, 2000-2050. AGU Fall Meeting 2022; Chicago, IL, USA; https://ui.adsabs.harvard.edu/abs/2022AGUFM.H36F..04Z/abstract

Other Publications
Zia, A. (0000-0001-8372-6090) & Oikonomou, P.D. (0000-0001-6612-0994). (2024). Early Warning and Early Action. 18-32. Digital Technologies for Environmental Peacebuilding: Horizon Scanning of Opportunities & Risks. United Nations Environment Program. ISBN: 978-92-807-4164-3. https://wedocs.unep.org/20.500.11822/45795

Posters
Zia, A. (0000-0001-8372-6090), Oikonomou, P.D. (0000-0001-6612-0994), Clemins, P.J. (0000-0002-7930-3025), Schroth, A.W. (0000-0001-5553-3208), Wshah, S. (0000-0001-5051-7719), & Rizzo, D.M. (0000-0003-4123-5028). (2023). Securing Clean Water in Transboundary River Basins through Open Science and Environmental Diplomacy: Piloting AI augmented Hydro-climatic Multi-hazard Early Warning Early Action Lead Systems. American Geophysical Union Fall Meeting 2023 (AGU23); San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1355225

Title: Integrate observations and field work in support of expanded evaluation data testbeds in the Northeast 
 
Principal Investigator: Asim Zia

Research Team: Patrick Clemins, Eric Roy, Scott Turnbull

Institution(s): University of Vermont

Abstract: This cross-cutting project will build a Northeast National Water Model (NWM) Applications Testbed that defines best practices and implementation frameworks for water resource forecasts that make use of NWM outputs and provides the cyberinfrastructure resources necessary to fuse a diverse array of data sources to force and evaluate the forecasts.  

The goal of the project is to support evaluation and research to operations (R2O) for the following three NWM application forecast use cases: (1) Improving the representation of floodplain effects on hydraulic routing and flood inundation modeling and mapping (flood forecasting); (2) Coupling novel low-cost spatially distributed nutrient sensors and NWM output to forecast nutrient loading and inform state implementation of EPA mandated nutrient reduction targets (nutrient forecasting); and (3) Forecasting the incidence and duration of harmful algal blooms (HABs) at daily, weekly, and seasonal scales (HABs forecasting).  

This work supports research on the development of water quantity and quality forecasts in the NWM with the following objectives: (1) Verify river stage time series and flood inundation dynamics at selected sites representative of different hydrogeomorphic settings to inform improved channel routing algorithms (flood forecasting); and (2) Improve the understanding of the relationships between streamflow and transported constituents in support of developing water quality forecasts (nutrient forecasting) and predicting the occurrence of harmful algal blooms (HABs forecasting).  

The Northeast NWM Application Testbed will expand and maintain a river and floodplain sensor network consisting of (1) High-frequency, low-cost sensor systems installed along select NHDplus reaches to monitor river stage and document floodplain inundation timing, duration, and extent; (2) Drone-based sensors to spatially enhance and contextualize at-a-point measurements of river stage; and (3) High-frequency turbidity sensors to guide the development of discharge-concentration relationships. The Testbed will also establish water resource forecasting best practices and implementation frameworks as well as the complementary cyberinfrastructure necessary to provide data and code for forecast evaluation, utilities and containers to analyze and reformat data, documentation for the various data sources, and operational user outreach and facilitation to help the CIROH forecast teams use the Testbed. The Northeast NWM Applications Testbed will serve as an integration and collaboration nexus for NWM applications researchers both regionally and nationally. Through the testbed services, including compute resources, data storage, a forecast workflow and protocol framework, software tools, and facilitation, the Testbed is catalyzing CIROH research across the three use cases by providing an accessible, easy-to-use, standardized set of evaluation data, models, and tools to researchers. In addition, the Testbed is convening discussions among regional and national CIROH participants around evaluation best practices including next generation evaluation metrics (information theory, event-based, and object-based) and error analysis and disaggregation methods. The Testbed will support establishment of new operational NWM application forecasts across the three initial test cases. In addition, as our Testbed users develop their NWM application forecasts, they can contribute to NextGen development by determining which NWM reach forecasts contribute most to the errors in their own NWM application forecast and inform which hydrology models are most appropriate for certain classes of reaches or in need of further development.
 
Publications, Presentations and Posters

Conference Presentations
Oikonomou, P.D. (0000-0001-6612-0994), Zia, A. (0000-0001-8372-6090), Clemins, P.J. (0000-0002-7930-3025), Turnbull, S. (0000-0002-4384-652X), Schroth, A.W. (0000-0001-5553-3208), Hannoun, K.I., Hannoun, I.A., Isles, P.D.F. (0000-0003-4446-6788), & Rizzo, D.M. (0000-0003-4123-5028). (2023). An Integrated Process-based Modelling Approach for Forecasting Lake Cyanobacteria Blooms Development: A Hindcast Experiment.. American Geophysical Union Fall Meeting 2023 (AGU23); San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1450905

Clemins, P.J. (0000-0002-7930-3025), Beckage, N.B. (0009-0000-9026-9510), Oikonomou, P.D. (0000-0001-6612-0994), Turnbull, S. (0000-0002-4384-652X), & Zia, A. (0000-0001-8372-6090). (2024). Computational Workflow Design for a Cyanobacterial Harmful Algal Bloom (CyanoHAB) Forecast Skill Elasticity Experiment. CIROH Training and Developers Conference 2024; Salt Lake City, UT; https://ciroh.ua.edu/abstracts/computational-workflow-design-for-a-cyanobacterial-harmful-algal-bloom-cyanohab-forecast-skill-elasticity-experiment/

Beckage, N.B. (0009-0000-9026-9510), Clemins, P.J. (0000-0002-7930-3025), Turnbull, S. (0000-0002-4384-652X), Oikonomou, P.D. (0000-0001-6612-0994), Adil, M. (0009-0006-7435-0531), Morales-Velázquez, M.I., & Zia, A. (0000-0001-8372-6090). (2024). Data Acquisition Framework Design for Environmental Modelling Input Data. 12th International Congress on Environmental Modelling and Software (iEMSs); East Lansing, MI; https://conference.iemss.org/timetable/event/c9/

Clemins, P.J. (0000-0002-7930-3025). (2024). Data Workflows 101. CIROH Developers Conference; Salt Lake City, UT; https://ciroh.ua.edu/devconference/hydrological-applications-of-machine-learning-workshops/data-workflows-101-acquisition-manipulation-and-visualization-2024/

Adil, M. (0009-0006-7435-0531), Oikonomou, P.D. (0000-0001-6612-0994), Rizzo, D.M. (0000-0003-4123-5028), Clemins, P.J. (0000-0002-7930-3025), Turnbull, S. (0000-0002-4384-652X), Schroth, A.W. (0000-0001-5553-3208), Isles, P.D.F. (0000-0003-4446-6788), Hannoun, K.I., Hannoun, I.A., Zia, A. (0000-0001-8372-6090), & Wshah, S. (0000-0001-5051-7719). (2023). Deep Learning Framework to Predict Harmful Algal Blooms by Leveraging Multi-Modal Data. American Geophysical Union Fall Meeting 2023 (AGU23); San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1422569

Oikonomou, P.D. (0000-0001-6612-0994), Yen, H. (0000-0002-5509-8792), Clemins, P.J. (0000-0002-7930-3025), Rizzo, D.M. (0000-0003-4123-5028), Schroth, A.W. (0000-0001-5553-3208), Turnbull, S. (0000-0002-4384-652X), & Zia, A. (0000-0001-8372-6090). (2023). Future Climate Impacts on a Highly Heterogeneous Watershed in Vermont. American Geophysical Union Fall Meeting 2023 (AGU23); San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1441066

Zia, A. (0000-0001-8372-6090), Schroth, A.W. (0000-0001-5553-3208), Isles, P.D.F. (0000-0003-4446-6788), Clemins, P.J. (0000-0002-7930-3025), Oikonomou, P.D. (0000-0001-6612-0994), Turnbull, S. (0000-0002-4384-652X), Beckage, B., Winter, J., & Rizzo, D.M. (0000-0003-4123-5028). (2024). Integrated Harmful Algal Bloom Early Warning Systems Can Quantify the Impact of Early vs. Delayed Policy Actions for Building Climate Resilience. 12th International Congress on Environmental Modelling and Software (iEMSs); East Lansing, MI; https://conference.iemss.org/timetable/event/c7/

Turnbull, S. (0000-0002-4384-652X). (2024). Introduction to Git. CIROH @ UVM 2024 Workshops; Burlington, VT, USA; https://uvmoffice-my.sharepoint.com/personal/water_uvm_edu/_layouts/15/stream.aspx?id=%2Fpersonal%2Fwater%5Fuvm%5Fedu%2FDocuments%2FVideo%2Fciroh%20seminars%2F2024%2D02%2D15%2DIntroduction%20to%20GIT%20with%20Scott%20Turnbull%2Emp4&ga=1&referrer=StreamWebApp%2EWeb&referrerScenario=AddressBarCopied%2Eview%2E44534fab%2D8f41%2D48a6%2Da85d%2Dab9d59347f09

Oikonomou, P.D. (0000-0001-6612-0994), Clemins, P.J. (0000-0002-7930-3025), Beckage, N.B. (0009-0000-9026-9510), Turnbull, S. (0000-0002-4384-652X), Schroth, A.W. (0000-0001-5553-3208), Rizzo, D.M. (0000-0003-4123-5028), & Zia, A. (0000-0001-8372-6090). (2024). Multi-Scale Forecast Skill Evaluation Framework for Integrated Early Warning Systems. 12th International Congress on Environmental Modelling and Software. 12th International Congress on Environmental Modelling and Software (iEMSs); East Lansing, MI; https://conference.iemss.org/timetable/event/d3/

Clemins, P.J. (0000-0002-7930-3025), Turnbull, S. (0000-0002-4384-652X), Zia, A. (0000-0001-8372-6090), Schroth, A.W. (0000-0001-5553-3208), Diehl, R.M. (0000-0001-9414-4045), & Wemple, B.C. (0000-0002-3155-9099). (2023). Northeast Evaluation Testbeds for Hydrologic Impacts Forecasting. American Geophysical Union Fall Meeting 2023 (AGU23); San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1412752

Clemins, P.J. (0000-0002-7930-3025) & Beckage, N.B. (0009-0000-9026-9510). (2024). Vermont Advanced Computing Center (VACC) Workflows. CIROH @ UVM 2024 Workshops; Burlington, VT, USA; https://uvmoffice-my.sharepoint.com/:v:/g/personal/water_uvm_edu/EYo_gH8fC4VNq749647qTucBdRVnhBtBTlJELnY6ynmrbA?e=ZYVvLZ

Other Publications
Zia, A. (0000-0001-8372-6090) & Oikonomou, P.D. (0000-0001-6612-0994). (2024). Early Warning and Early Action. 18-32. Digital Technologies for Environmental Peacebuilding: Horizon Scanning of Opportunities & Risks. United Nations Environment Program. ISBN: 978-92-807-4164-3. https://wedocs.unep.org/20.500.11822/45795

Posters
Zia, A. (0000-0001-8372-6090), Clemins, P.J. (0000-0002-7930-3025), Oikonomou, P.D. (0000-0001-6612-0994), Turnbull, S. (0000-0002-4384-652X), Schroth, A.W. (0000-0001-5553-3208), & Rizzo, D.M. (0000-0003-4123-5028). (2022). Information Theoretic Approaches to Characterize Uncertainty in Computational Models of Coupled Human and Natural Systems: Insights from an Integrated Model Predicting Water Quality in Lake Champlain under Alternate Hydro-Climatic, Land Use, And Nutrient Management Conditions. American Geophysical Union (AGU) annual conference; Chicago IL, USA; https://agu.confex.com/agu/fm22/meetingapp.cgi/Paper/1148337

Zia, A. (0000-0001-8372-6090), Oikonomou, P.D. (0000-0001-6612-0994), Clemins, P.J. (0000-0002-7930-3025), Schroth, A.W. (0000-0001-5553-3208), Wshah, S. (0000-0001-5051-7719), & Rizzo, D.M. (0000-0003-4123-5028). (2023). Securing Clean Water in Transboundary River Basins through Open Science and Environmental Diplomacy: Piloting AI augmented Hydro-climatic Multi-hazard Early Warning Early Action Lead Systems. American Geophysical Union Fall Meeting 2023 (AGU23); San Francisco, CA; https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1355225

Clemins, P.J. (0000-0002-7930-3025), Zia, A. (0000-0001-8372-6090), Adil, M. (0009-0006-7435-0531), Wshah, S. (0000-0001-5051-7719), O’Neil-Dunne, J.P. (0000-0002-5352-7389), Oikonomou, P.D. (0000-0001-6612-0994), Rizzo, D.M. (0000-0003-4123-5028), Schroth, A.W. (0000-0001-5553-3208), Isles, P.D.F. (0000-0003-4446-6788), Hannoun, K.I., Hannoun, I.A., Blocher, S., Turnbull, S. (0000-0002-4384-652X), Beckage, N.B. (0009-0000-9026-9510), Bailey, M., Unveren, H., Duffaut-Espinosa, L. (0000-0003-4363-5375), & Pinder, G.. (2024). Using Remote Sensing Data to Train, Evaluate, and Adapt Harmful Algal Bloom Forecast Models. AGU Chapman Conference 2024; Honolulu, HI, USA; https://agu.confex.com/agu/24chapman1/meetingapp.cgi/Paper/1493708