In times of an increasingly uncertain climate and growing demand for water resources, there is a need for assessing how aquatic systems worldwide are responding to co-occurring human and climatic disturbances. Over the past half century, remote sensing technology, high performance and cloud computing infrastructure, machine learning techniques, open data practices, and widened training in computer programming have created extraordinary opportunities to expand aquatic science research across spatial and temporal scales. Remote sensing of chlorophyll concentrations has enabled near-global monitoring of algal blooms. Data science and machine learning methods have empowered the prediction of water quality dynamics, while also informing management actions. Process-based models have improved our understanding of environmental fluid dynamics, which can be consequential for ecosystem regime shifts. Open data practices have increased the amount of publicly available data that many calibration and validation techniques ultimately demand. The nexus of data science, remote sensing, modeling, and open science in the aquatic sciences is a dynamic, rapidly progressing space, where novel research questions can be answered at unprecedented scales.
Given these developments, there can be a lingering question of "What’s next?” To date, many aquatic remote sensing, modeling, and data science efforts have focused on measuring or predicting individual parameters or variables. While these individual efforts are herculean initiatives, a ripe frontier for this nexus is linking limnological, hydrological, oceanographic, and ecological processes and principles with remote sensing, data science, and modeling techniques to understand fundamental properties of aquatic systems and inform monitoring at local-to-global and subdaily-to-decadal scales. Additionally, the increase of open science and data democratization brings the potential for a more novel and far-reaching scientific research and expanding community.
To build a conversation around multifaceted developments in the aquatic sciences, this session invites contributors to share how they use one or a combination of remote sensing, data science, modeling, or open science practices to expand the fields of limnology, oceanography, hydrology, or ecology. We envision this session will host a range of presentation topics, including but not limited to novel methods for atmospheric corrections, scaling of cloud and other high-volume computing environments, assessing water quantity and quality across spatial and temporal scales, quantifying long-term changes in mixing dynamics, merging process-based modeling and deep learning, and applying data-intensive techniques for basic and applied research questions. While submissions may stem from methodological and technical hurdles encountered in remote sensing and data science fields, we challenge submissions to focus on how their efforts de-silo the aquatic sciences from the remote sensing and data science arenas.
Lead Organizer: Robert Ladwig, Aarhus University (rladwig@ecos.au.dk)
Co-organizers:
Michael Meyer, U.S. Geological Survey (mfmeyer@usgs.gov)
Rachel Pilla, Oak Ridge National Laboratory (pillarm1@ornl.gov)
Laura Soares, Helmholtz Centre for Environmental Research (laura.soares@ufz.de)
Marina Amadori, University of Trento (marina.amadori@unitn.it)
Presentations
04:30 PM
FROM ECHOES TO ESTIMATES: MAPPING AND PREDICTING SUBMERGED MACROPHYTE BIOMASS IN A TROPICAL RESERVOIR (10280)
Primary Presenter: Wagner Chiba de Castro, Federal University of Latin American Integration (wagner.castro@unila.edu.br)
Submerged aquatic macrophytes play essential roles in lentic ecosystems, reflecting environmental quality and serving as effective bioindicators. However, excessive growth can impair ecosystem services. Traditional monitoring methods are costly and spatially limited, whereas hydroacoustics provides a rapid and non-destructive approach for large-scale assessments. This project evaluates the influence of seasonality and environmental quality on macrophyte biomass, species richness, and abundance in the Itaipu Reservoir, a strategic binational system. We integrate hydroacoustic data with water and sediment quality measurements and plant biomass. Preliminary results show no effect of depth (up to 4 m) or seasonality on biomass. A Gamma GLM revealed a significant relationship between hydroacoustic plant height and biomass. Among the seven submerged species identified, Egeria najas Planch. and Hydrilla verticillata (L.f.) Royle were the most abundant, and a previously unrecorded species was described. A predictive model combining hydroacoustic, physicochemical, and biological parameters is being developed to enhance the precision and scalability of macrophyte monitoring. Integrating these data provides a comprehensive understanding of ecological patterns, offering a robust tool for reservoir management.
04:45 PM
HIERARCHICAL ECO-ZONE DELINEATION IN DATA-SCARCE AFROTROPICAL RIVER LANDSCAPES USING OPEN DATA MODELLING FRAMEWORKS (10986)
Primary Presenter: Amon Aine, University of Natural resources and life Sciences (aine3amon@gmail.com)
Understanding spatial ecological heterogeneity is critical for modelling and managing aquatic ecosystems, yet most Afrotropical environmental classifications remain limited to drainage divides or coarse expert-based schemes. These approaches overlook key spatial processes that control aquatic conditions across landscapes, especially in data-scarce regions Here, we integrated global remote sensing datasets, open environmental data, and multiple machine-learning algorithms within an open-source workflow to delineate hierarchical eco-zones for Uganda’s lake, riverine, and wetland landscapes. Climate variables, topography, and hydrologically relevant terrain metrics were used to delineate template ecological regions, which were validated by examining patterns of potential natural vegetation (PNV) through zonal statistics to assess ecological heterogeneity across scales. The workflow produced four nested eco-regional maps, with five major ecoregions at the broadest level and 3 finer bioregional subdivisions. 10-fold cross-validation using PNV distributions demonstrated robust predictive performance despite regional data limitations. This framework demonstrates how scalable, cloud-ready modelling tools and open data can provide spatially coherent ecological stratification for hydrological modelling, bioassessment, water quality prediction, and biodiversity monitoring. The approach is transferable to other data-scarce tropical countries, enhancing the ability to design region-specific ecological assessments, identify vulnerability hot
05:00 PM
Integrating modeling, remote sensing and in-situ data to characterize hydro-sedimentological dynamics and climate impacts in the Río de la Plata estuary (11033)
Primary Presenter: Pablo Santoro, Universidad de la República (psantoro@fing.edu.uy)
This work synthesizes a long-term effort to characterize the Río de la Plata, a large microtidal estuary where sediment-rich waters from the Paraná and Uruguay rivers mix with shelf waters. Over recent decades, we combined high-resolution 3D numerical modeling, continuous in-situ monitoring, and remote sensing to investigate governing physical processes. Initial research evolved from nested models capturing tide-surge interactions to fully coupled wave-current-sediment simulations incorporating self-weight consolidation. This established that at shorter scales, fronts respond to local storms and meteorological tides propagating along the continental shelf. To expand this characterization to decadal climate variability, we integrated this framework with remote sensing. Processing satellite imagery spanning two decades revealed the complex spatio-temporal dynamics of the turbidity front. As cohesive sediments flocculate, this front serves as a proxy for salinity intrusion, validated by correlating satellite-derived locations with in-situ data and incorporating salinity-dependent settling velocity in models. This multi-scale approach reveals that while intra-annual variability is governed by seasonal winds, frontal positions show clear interannual dependence on river discharge modulated by ENSO and low-frequency climate oscillations. This cumulative integration enables a robust understanding of the estuary's fundamental properties, moving beyond isolated analyses to a comprehensive mechanistic characterization.
05:15 PM
INFERRING MULTITROPHIC CARBON DYNAMICS IN THE BERMUDA ATLANTIC TIME-SERIES STUDY USING A MECHANISTIC ECOSYSTEM MODEL (11468)
Primary Presenter: Falk Eigemann, TU Berlin (falkeigemann@gmail.com)
Modern sampling and analysis tools generate extensive high-resolution data on microbial communities, including DNA sequences, cell abundances, and metabolite concentrations. Yet direct observation of microbial interactions, which are essential for a quantitative, mechanistic understanding, and the pathways through which carbon flows between them, remain difficult to observe directly and require quantitative inference. To address this gap, we integrated four years of data from the Bermuda Atlantic Time-series Study (BATS) into FluxNet, a mechanistic ecosystem model that estimates carbon fluxes among species and trophic compartments. The model represents approximately forty of the most abundant taxa across heterotrophic bacteria, phytoplankton, heterotrophic protists, and zooplankton, as well as particulate and dissolved organic matter, and reconstructs interaction networks by constraining carbon transfer in a mass-balanced framework. To capture vertical structure, the upper two hundred meters of the water column are divided into dynamically varying surface, deep chlorophyll maximum, and upper mesopelagic layers, allowing analysis of vertical transport and export processes. The resulting time-resolved flux fields provide a framework for examining how carbon moves within and between layers, how resources are divided among trophic groups, and when export toward deeper waters intensifies. By combining detailed time series observations with a mechanistic, multispecies framework, this approach provides a quantitative view of multitrophic carbon pathways in the surface ocean and offers a basis for interpreting ecosystem variability at BATS.
05:30 PM
ASSESSING IMPACTS OF SEA-LEVEL RISE ON ALBEMARLE-PAMLICO SOUND, NORTH CAROLINA: FOUNDATIONS FOR ECOLOGICAL IMPLICATIONS (11848)
Primary Presenter: Katherine Boot, University of North Carolina Wilmington (keb3424@uncw.edu)
The Albemarle-Pamlico Sound (APS) lagoonal estuary in northeastern North Carolina is the second largest estuarine system in the United States. APS is a dynamic system influenced by seasonal and climate-driven environmental changes, including sea-level rise (SLR). To investigate hydrodynamic responses to these changes, a hindcast simulation for 2019 was developed using Semi-Implicit Cross-Scale Hydrodynamic System Model (SCHISM) and validated with in-situ observations, demonstrating model fidelity. The model grid spatial resolution ranges from ~10 m in river channels to ~4 km in the open ocean and extends ~400 km offshore into the Atlantic Ocean. Inputs include atmospheric forcings, tides, river discharge, and open ocean boundary conditions. We assessed the impacts of SLR by isolating changes in mean sea level (MSL) and examining the resulting hydrodynamic variations in APS under two scenarios: a moderate SLR scenario (CMIP6 SSP370) and a high SLR scenario (SSP585). Model results indicate that MSL changes significantly influence seasonally varying conditions, with spatially heterogeneous impacts on hydrodynamic circulation, salt flux, stratification, and water quality parameters in APS. These findings provide insights into the physical processes driving system responses to SLR. Because these hydrodynamic responses vary spatially, the resulting ecological impacts are also likely to be heterogeneous, influencing habitat suitability and resilience for fisheries-relevant species such as southern flounder (Paralichthys lethostigma), a key fisheries species in the region.
05:45 PM
Bioaccumulation and Subcellular Distribution of Metals Across Species through Quantitative Ion–Character Relationship Modelling (11207)
Primary Presenter: Cândido Rodrigues, Université du Québec à Montréal (carvalho_rodrigues.candido@courrier.uqam.ca)
Metal contamination poses ecological risks to aquatic biota, especially when toxicological data are scarce and metals are not regulated. As metal toxicity is closely linked to their intracellular distribution, we developed Quantitative Ion Character-Activity Relationship (QICAR) models to predict metal bioaccumulation and subcellular compartmentalization in sensitive (mitochondria, microsomes and lysosomes, cytosolic heat-denatured proteins) and detoxified (granules, cytosolic heat-stable proteins) compartments. Our dataset included measurements for 34 metals across nine fish, one amphipod and one mammal, combined with 21 metal descriptors covering physical, electronic, binding, and redox properties. Data were modelled by linear multiple regressions, Generalized Additive tensor models and CatBoost, evaluated using leave-one-species-out (LOSO) cross-validation. Tree‑based models exhibited the best predictive performance, with mean LOSO R² values ranging from 0.77 to 0.84 across endpoints. Atomic number, Ionic index, and Covalent index captured the majority of the variance. Predicted bioaccumulation was consistent with observed values in both nmol/g and subcellular fraction contributions (%). Application of the model to 66 additional metals confirmed its reliability. SHAP explanations revealed three metal groups based on how accumulation is modelled. Thus, QICARs provide a novel and robust framework for predicting multi‑species metal accumulation and intracellular behaviour, with insights into how differential subcellular distribution relates to metal chemical properties.
SS009C The Next Frontier in Aquatic Sciences: Linking Remote Sensing, Data Science, Modeling, and Open Science to Better Understand Aquatic Ecosystems
Description
Time: 4:30 PM
Date: 15/5/2026
Room: 525AB