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 aquatic science 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 emergent 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 diverse, inclusive, and accessible scientific community and more holistic research.
To build a conversation around multifaceted developments in remote sensing, data science, modeling, and 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 stratification 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.
We enthusiastically encourage submissions by early career researchers as well as by researchers from BIPOC, LGBTQIA+, and other marginalized identities. An intentional focus on research that breaks down barriers to entry for underrepresented scientists in the fields of remote sensing and aquatic data science, through open science, will yield insight into the power and potential of the next frontier in emergent properties of aquatic systems.
Lead Organizer: Michael Meyer, U.S. Geological Survey (mfmeyer@usgs.gov)
Co-organizers:
Elisa Calamita, Eawag (elisa.calamita@eawag.ch)
Kate Fickas, Esri (kfickas@esri.com)
Robert Ladwig, University of Wisconsin - Madison (rladwig2@wisc.edu)
Rachel Pilla, Oak Ridge National Laboratory (pillarm1@ornl.gov)
Presentations
04:00 PM
Sea level rise facilitates increased cross-reef transport at the reef-scale (7818)
Primary Presenter: Eleanor Mawson, The Lyell Centre, Heriot-Watt University, Edinburgh, Scotland, UK. (em2029@hw.ac.uk)
An accurate representation of the effects of rising sea levels on coral reef hydrodynamics is crucial to examine retention and export mechanisms, highlight fine-scale ecological changes, and evaluate cross-reef transport. This study investigates how hydrodynamics are governed by rising sea levels within a coral reef atoll (One Tree Reef), located off the coast of Gladstone on the Southern Great Barrier Reef. By utilising present-day conditions and IPCC SSP scenarios, a Delft3D model was employed to simulate reef hydrodynamics under various sea level rise projections (28 cm and 101 cm). The model was validated against tidal gauges and field observational data. Depending on the phase of the tidal cycle, the results revealed a gradual decrease in ubot and an increase in velocity across OTR. Higher water levels lead to decreased wave forces especially towards the eastern reef crest; higher velocity intensifies eddies, and promotes transport of nutrients, sediment and larvae across the reef. This enhances turbidity, promotes algae growth, and may sweep larvae away from the reef prior to settlement. Continued sea level rise will further enhance these changes and processes experienced at finer scales will be altered or forced to adapt to remove possible reduction in the function of coral reef ecosystems.
04:15 PM
Exploring changes in tidal dynamics in intertidal environments under sea level rise scenarios in the Wadden Sea (SE North Sea) (8017)
Primary Presenter: Gaziza Konyssova, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (gaziza.konyssova@awi.de)
Recent studies in the Wadden Sea (SE North Sea) indicate a complex non-linear feedback between sea-level rise (SLR), tidal dynamics and geomorphology, posing a threat to the functioning of coastal ecosystems and the provision of ecosystem services in particular. This study focuses on the SLR-related changes in intertidal dynamics, specifically tidal inundation, tidal asymmetry, and circulation patterns. The Sylt-Rømø Bight has been selected as a case study site. The scenarios based on the projected bathymetric evolution and on mean SLR under low and high emission pathways (RCP 2.6 and RCP 8.5) were simulated using the coastal hydrodynamic model FESOM-C running on an unstructured, high-resolution mesh (up to 2 m in wetting-drying zones). Analysis reveals that only 2.2% to 3.4% (13∙106 and 21∙106 m2) of intertidal flat areas will be permanently submerged until 2050 under low and high emissions scenarios, respectively. On the contrary, by the end of the century, submergence increases up to 4.8% to 13.9% (29∙106 and 84∙106 m2). Remarkable are also the results showing that the main tidal channels experience different, even opposing, changes in mean and peak current velocities, which point to changes in transport pathways and possible changes in local erosion rates. Such spatial inhomogeneity is also depicted in shifts in the tidal asymmetry, indicating an overall transition towards a lagoon-like system as SLR accelerates. These findings underscore the importance of understanding the complex interplay of the hydrodynamic processes driving coastal dynamics and habitat change.
04:30 PM
Exploring relationships between avian biodiversity and greenhouse gas flux in Canadian Prairie wetlands (8110)
Primary Presenter: Samuel Woodman, University of Lethbridge (samuel.woodman@uleth.ca)
We are experiencing a dual global crisis: climate change and biodiversity loss. Wetland protection and restoration have been considered to both enhance carbon (C) sequestration and support greater biodiversity. Yet a trade-off may exist between wetland biodiversity and greenhouse gas (GHG) emissions that limit C sequestration, because wetland plant productivity that supports greater biodiversity may also fuel GHG emissions, namely that of methane. To explore whether such a trade-off exists, we surveyed wetland avian diversity, GHG emissions, and emergent vegetation density in wetlands of the Canadian Prairie Pothole Region. Rates of GHG emissions from aquatic habitats surveyed from 2021 to 2023 were compared with species richness detected with autonomous recording units. We combined these approaches with estimates of emergent macrophyte (EM) biomass derived from ground-based surveys and high-resolution UAV imagery scaled to the entire wetland using machine learning and cluster analysis. Overall, wetlands that supported greater avian species richness tended to have lower rates of carbon dioxide emissions and were uncorrelated with methane emissions.These findings suggest that efforts to protect wetlands for the purpose of biodiversity preservation may not engender trade-offs in the carbon sequestration benefits from these systems.
04:45 PM
MACHINE LEARNING AND REMOTE SENSING APPROACHES TO DEVELOP HIGH-RESOLUTION SPATIAL PREDICTION OF AQUATIC INVASIVE SPECIES IN LAKES (7895)
Primary Presenter: Kateri Salk, Tetra Tech (kateri.salkgundersen@tetratech.com)
Aquatic invasive species (AIS) have significant negative impacts on lake ecosystems, underscoring the need for improved detection and management. However, on-the-ground monitoring efforts are time- and resource-intensive. Recent advancements in satellite-based technology and machine learning algorithms present a promising pathway to predict AIS presence within and across lakes. The goal of this study was to train a statistical model to predict AIS presence within lakes. First, a large spatial dataset was created that included areas of mapped AIS locations, sonar-derived bathymetric conditions, proximity to anthropogenic points of interest (e.g., boat launches, beaches, campsites), and adjacent land cover. Then, three statistical models (linear regression, tree-based machine learning model, artificial neural network) were tested head-to-head to determine the model that best predicted AIS. The machine learning model (XGBoost) had the best model performance, correctly predicting AIS presence in ¾ of locations. The most important predictor variables were proximity to shoreline, forest, impervious cover, and agricultural land cover. This final model selected also minimized false negatives, an important outcome as this model will be used to guide monitoring activities by The Nature Conservancy. The model was applied to thousands of lakes in Adirondack Park, enabling prioritization of monitoring efforts for early detection surveys and other mitigation measures as well as serve as an invasive species communication tool for stakeholders and the public in the Adirondack region.
05:00 PM
Uniting Remotely Sensed and In Situ Data to Understand Ecosystem Function in Lake Mendota: a GLEON-NASA Collaboration (7991)
Primary Presenter: Caroline Owens, University of California Santa Barbara (carolinehowens@gmail.com)
The use of remotely sensed data to study lake ecosystem function requires relating these data to in situ measurements, like metabolism and the microbial community that drives it. Routine hyperspectral imagery enabling this comparison will be provided globally by the upcoming NASA Surface Biology and Geology mission. However, relatively few studies have verified possible uses of these data over smaller inland waters, in part due to the scarcity of coincident in situ data. We use existing long-term limnological data and team science to address this research gap through a partnership between the Global Lake Ecological Observatory Network and NASA. Our team united expertise from the fields of aquatic science and remote sensing at Lake Mendota, a well-studied eutrophic lake in Madison, WI, USA. We used a range of statistical and modeling techniques to assess relationships between remotely sensed imagery, 16S rRNA microbial data, and modeled metabolic data. We found notable seasonal relationships between multispectral imagery and modeled gross primary productivity (GPP). We also identified microbial community composition as a strong predictor of modeled GPP and respiration. Finally, we related the top principal components from hyperspectral scenes (DESIS, PRISMA, and AVIRIS) to metabolic processes and key microbial groups. Given the growing need for water quality monitoring in inland waters and the increasing availability of hyperspectral data, our study demonstrates how high-resolution remote sensing data can reflect metrics of ecological function and water quality.
05:15 PM
Assessing 35 years of lake trophic state change in 55,000 lakes across the contiguous US (7733)
Primary Presenter: Michael Meyer, U.S. Geological Survey (mfmeyer@usgs.gov)
Lakes are integrators of autochthonous and allochthonous processes. Eutrophication and dystrophication are examples of how changes to autochthony and allochthony can lead to shifts in trophic state. Despite being a fundamental limnological metric, understanding the broad spatial and temporal patterns of trophic state and its drivers has been limited. To this end, we combined Landsat optical reflectance values with in situ true color and phosphorus data from the U.S. Environmental Protection Agency's National Lake Assessment to build classification models of trophic state. We applied those models to 55,662 lakes across the contiguous U.S. from 1984 to 2020, and then related regional trophic state change with a suite of hydrologic, climatic, terrestrial, biological, and atmospheric deposition predictors. Over the 35 years, we found that the majority of lakes greater than 10 ha in the US are consistently mixotrophic/eutrophic (~55%), yet dystrophic lakes have substantially increased nationally. This “brownification” trend is concentrated in the Upper Midwest and Northern Appalachian regions and may be associated with diverging mechanisms. Whereas browning in the Northern Appalachians is associated with wetland cover and acid rain legacy, the Upper Midwest browning trends appear more associated with wetland cover, warming air temperatures, and increasing precipitation. Together, these results support previous findings at regional and national scales and suggest that varied, complex drivers may be causing this change.
SS01C - The Next Frontier in Aquatic Sciences: Linking Remote Sensing, Data Science, Modeling, and Open Science to Understand Ecosystems’ Emergent Properties
Description
Time: 4:00 PM
Date: 5/6/2024
Room: Hall of Ideas G