Over the past several decades, advances in satellite remote sensing, autonomous sensor networks, as well as robust modeling frameworks have collectively created extraordinary opportunities to expand aquatic science research across spatial and temporal scales. With an added emphasis on open science, the gradual integration of big data analyses and increasing access to intensive computing infrastructures offer potential for a more diverse, inclusive, and globally connected scientific community. Moreover, the harmonization of diverse data streams is a ripe frontier that can help link limnological, hydrological, oceanographic, and ecological processes and principles to understand fundamental, applied, and emerging areas of research at local-to-global and subdaily-to-decadal scales.

This session seeks to broadly address open aquatic science initiatives to build a conversation around multifaceted developments in remote sensing, modeling, and other data intensive areas of research. We envision this session will host a range of presentation topics, including but not limited to novel applications of remote sensing, scaling analyses in cloud and other high-volume computing environments, assessing water quantity and quality through space and time, quantifying long-term changes in physical dynamics, merging process-based modeling and deep learning, and applying data-intensive techniques for basic and applied research questions. While submissions may focus on methodological and technical hurdles encountered in remote sensing and data science fields, we encourage submissions that seek to advance holistic application of these tools in the aquatic sciences, thereby advancing basic science questions with applied science methods.

We enthusiastically invite 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: Lorena Silva, University of Missouri and University of Maryland Center for Environmental Science (lsilva@umces.edu)

Co-organizers:

Greg Silsbe, University of Maryland Center for Environmental Science (gsilsbe@umces.edu)

Michael Meyer, Hydrologic Remote Sensing Branch - Observing Systems Division, U.S. Geological Survey (mfmeyer@usgs.gov)

John Gardner, University of Pittsburgh (gardner.john@pitt.edu)

Presentations

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04:30 PM

FUTURE OXYGEN DYNAMICS IN DIVERSE GLOBAL LAKES (9415)

Primary Presenter: Lipa Nkwalale, Helmholtz Center for Environmental Research (lipa.nkwalale@ufz.de)

The long-term impacts of climate change on lakes worldwide are expected to significantly alter both water quantity and quality, leading to changes in thermal regimes, ecosystem functioning, habitats, and biogeochemical cycling. While recent advances in global-scale models have projected rising surface water temperatures and extended stratification periods, these models often overlook key aspects of water quality and are limited by their focus on single mechanistic approaches, leading to uncertainties. Additionally, most existing ecological models lack the transferability needed for global-scale applications, as they are tailored to specific lake systems. In this study we use a multi-model approach in which three lake models were forced by five different climate model projections to simulate future changes in the stratification duration and hypolimnion temperature patterns of 73 lakes (2015 – 2100) under three Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, 3-7.0, and 5-8.5 using data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). We apply a deep-water dissolved oxygen (DO) depletion model, projecting future hypolimnion DO concentrations at the end of summer stratification for the 73 lakes. Our projections quantify future declining DO levels, especially under SSP-3.70 and 5.85, on a global scale due to rising hypolimnion temperatures affecting depletion rates and longer stratification periods. Mitigation strategies should be considered to preserve ecosystem functioning.

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04:45 PM

SIMONS COLLABORATIVE MARINE ATLAS PROJECT (CMAP): AN INTEGRATIVE OCEANOGRAPHIC DATA PORTAL SUPPORTING THE INTERCONNECTION OF DIVERSE OCEAN DATASETS (9425)

Primary Presenter: Tansy Burns, University of Washington (tansy@uw.edu)

Satellite remote sensing and autonomous sensor networks provide rich environmental data. Interconnecting these data with field expedition data expands on the insight provided by any of these datatypes alone. However, collective analyses of these heterogeneous collections are complex as datasets are scattered across distributed repositories with various file formats. To address this issue, we created an open-source data portal, Simons Collaborative Marine Atlas Project (Simons CMAP) that provides interconnected, harmonized ocean data. To reduce difficulties faced by researchers when finding, retrieving, and using publicly available data we designed Simons CMAP around three key principles: (1) The Simons CMAP database includes diverse ocean datasets in a single location. These include multi-decadal global satellite products, biogeochemical numerical model outputs, and field observations. (2) The datasets are organized in a clean and unified data structure, ensured via multiple validation steps. This significantly accelerates data analysis and facilitates the dataset interconnection procedures. (3) Standard APIs provide database access, enabling the use of both a web-based application and programming interfaces that support multiple coding languages. These applications support use by those with varying technological comfort levels. Here we highlight how Simons CMAP facilitates the interconnection of satellite data with in-situ and model data, supporting analyses across varying temporal and spatial scales.

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05:00 PM

Methods and Estimates of Gas Exchange for National Ecological Observatory Network (NEON) Streams (8747)

Primary Presenter: Kaelin Cawley, NEON project operated by Battelle (kcawley@battelleecology.org)

Accurate estimation of gas exchange rates across the air-water boundary is critical in quantifying stream metabolic processes (e.g., primary production and respiration) as well as emission of greenhouse gas fluxes (e.g., carbon dioxide, methane, and nitrous oxide).  Collection and processing of NEON measurements are standardized, and data are quality-controlled, and freely available through a publicly accessible online data portal (www.data.neonscience.org). With at least 3 years of Reaeration (DP1.20190.001) and Continuous discharge (DP4.00130.001) data for each NEON stream site, we have begun evaluating the relationship between metrics, such as k₆₀₀, travel time, mean wetted width, and mean depth, with continuous discharge. The relationships between these metrics and discharge vary across site types with some showing increasing gas exchange rates with increasing discharge (e.g., LeConte Creek in Great Smokey Mountains National Park) while others do not show a strong relationship with discharge (e.g., Caribou Creek in Alaska). The range of k₆₀₀ values cover a wide range, from < 3 m/d at a low gradient stream (Pringle Creek) to > 100 m/d at a high gradient stream during high discharge (Pringle Creek). This presentation outlines the methods and results used by NEON to calculate gas exchange metrics using rearRate, an R software package and Bayesian model developed for working with NEON Reaeration (DP1.20190.001)  data, which is publicly available on GitHub at www.github.com/NEONScience/NEON-reaeration.

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05:15 PM

Quantification of Non-Perennial Stream Flow using Time-lapse Photography and Machine Learning (9739)

Primary Presenter: Jessica Wilhelm, University of Kansas (wilhelmjf@ku.edu)

While methods for measuring perennial streamflow are established in ecohydrological studies, methods for measuring non-perennial flow are still in development, and remain vastly underexplored. Understanding flow patterns in non-perennial streams requires new methods including quantifying sustained wet-up, long periods of low flow, and intense bursts of stormflow. In Kansas, over half of the streams are non-perennial due to the stark contrasts between wet- and dry- seasons. Here, we assessed the accuracy and suitability of an ecohydrologic method that combines time-lapse photography with machine learning techniques (i.e., gaugeCam) to quantify non-perennial streamflow. Our overarching question is, What conditions optimize the utility of ground-based time-lapse imagery and machine learning for quantifying non-perennial streamflow rates and connectivity? We installed trail cameras at the Konza Prairie Biological Station near Manhattan, Kansas, and translated their images into stream connectivity metrics using GaugeCam Remote Image Manager-Educational AI (GRIME AI), and built rating curves using GaugeCam Remote Image Manager-Educational 2 (GRIME2) and discharge data. We found that hydrologic metrics derived from gaugeCam images were useful for quantifying stream connectivity, providing a data-intensive technique to address the technical hurdles with assessing water quantity through space and time. Thus, time-lapse photography and machine learning serve as a novel application for quantifying long term changes in the physical dynamics of streams, including non-perennial systems.

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