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

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

<sup>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</sup>

 
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 

Insitution(s): University of Vermont, USGS

<sup>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</sup>

 
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

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

<sup>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</sup>

 
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 

Insitution(s): University of Vermont, USGS 

<sup>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</sup>

 
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 

Insitution(s): University of Vermont 

<sup>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</sup>

 
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

Insitution(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

<sup>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</sup>

 
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

Insitution(s): University of Vermont

<sup>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</sup>