Global Water Clarity: Continuing a Century-Long Monitoring

Lee, Zhongping; Arnone, Robert; Boyce, Daniel G.; Franz, Bryan A.; Greb, Stephen F.; Hu, Chuanmin; Lavender, Samantha; Lewis, Marlon R.; Schaeffer, Blake A.; Shang, Shaoling; Wang, Menghua; Wernand, Marcel Robert; Wilson, Cara

doi:10.1029/2018eo097251

Cited by 20 publications

(18 citation statements)

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“…Water clarity can also be observed from sensing technologies via aircraft and satellites, which greatly expand the geographic coverage of lakes that can be studied globally (Lee et al. ). Citizen scientists are further increasing the spatial coverage of observations by measuring water clarity with mobile devices (e.g., the Lake Observer smartphone app; http://lakeobserver.org, Graham et al.…”

Section: Increasing Collaboration Between Computer Scientists and Ecomentioning

confidence: 99%

“…), extracting usable information from complex and diverse data sources, and connecting patterns in the data to ecological processes (Lee et al. ). These challenges create many opportunities for collaboration between ecologists and computer scientists, including the development of cyberinfrastructure (e.g., virtual private network software and cloud computing) that is adaptable to a variety of data streams from a diversity of environmental observatories and facilitates findable, accessible, interoperable, and re‐usable data (FAIR data; Wilkinson et al.…”

Section: Increasing Collaboration Between Computer Scientists and Ecomentioning

confidence: 99%

“…Consequently, the volume and variety of data on lake water clarity and other water spectral characteristics are growing exponentially, as these data are indicator metrics of water quality (Watras et al 2015, Birgand et al 2016. Water clarity can also be observed from sensing technologies via aircraft and satellites, which greatly expand the geographic coverage of lakes that can be studied globally (Lee et al 2018). Citizen scientists are further increasing the spatial coverage of observations by measuring water clarity with mobile devices (e.g., the Lake Observer smartphone app; lakeobserver.org, Graham et al 2011).…”

Section: Benefits To Ecologymentioning

confidence: 99%

“…While technologies informed by computer science have enabled the rapid accumulation of water clarity big data (as defined by the 5Vs: data velocity, volume, value, variety, and veracity; Demchenko et al 2014), transforming those data more rapidly into new ecological knowledge and understanding of lake ecosystems require further computer science collaboration. In particular, major challenges that remain include transmitting ecological data from sensors to discoverable and accessible repositories, converting raw data to meaningful ecological variables (e.g., translating electrical voltages from fluorescent sensors into phytoplankton biomass concentrations; Roesler et al 2017), extracting usable information from complex and diverse data sources, and connecting patterns in the data to ecological processes (Lee et al 2018). These challenges create many opportunities for collaboration between ecologists and computer scientists, including the development of cyberinfrastructure (e.g., virtual private network software and cloud computing) that is adaptable to a variety of data streams from a diversity of environmental observatories and facilitates findable, accessible, interoperable, and re-usable data (FAIR data; Wilkinson et al 2016); the adoption of software techniques and technologies within the ecological research community (e.g., modeling in R); the development of computing power that scales with the size of the data and the demands of the models (Subratie et al 2017, Turner andCarpenter 2017); and the melding of data-driven models with theory-based ecological models that facilitate both data mining and interpretation (Karpatne et al 2017).…”

Section: Benefits To Ecologymentioning

confidence: 99%

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Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

et al. 2019

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In the era of big data, ecologists are increasingly relying on computational approaches and tools to answer existing questions and pose new research questions. These include both software applications (e.g., simulation models, databases and machine learning algorithms) and hardware systems (e.g., wireless sensor networks, supercomputing, drones and satellites), motivating the need for greater collaboration between computer scientists and ecologists. Here, we outline some synergistic opportunities for scientists in both disciplines that can be gained by building collaborations between the computer science and ecology research communities, with a focus on the benefits to ecology specifically. We also identify past contributions of computer science to ecology, including high‐frequency environmental sensor technology, advanced supercomputing capacity for ecological modeling, databases for long‐term and high‐frequency datasets, and software programs for ecological analyses, to anticipate future potential contributions. These examples highlight the power and potential for further integration of computer science technology and ideas into the ecological research community. Finally, we translate our own experiences working together as a team of computer scientists and ecologists over the past decade into a conceptual framework with recommendations for supporting productive collaborations at the interface of the two disciplines. We specifically focus on how to apply best practices of team science for bridging computer science and ecology, which we advocate will substantially benefit ecology long‐term.

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Section: Increasing Collaboration Between Computer Scientists and Ecomentioning

confidence: 99%

Section: Increasing Collaboration Between Computer Scientists and Ecomentioning

confidence: 99%

Section: Benefits To Ecologymentioning

confidence: 99%

Section: Benefits To Ecologymentioning

confidence: 99%

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Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

et al. 2019

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“…Elevated concentrations of sand, silt, clay, and organic matter can attenuate light, impact stream productivity, and may alter dissolved oxygen concentrations and water temperatures (Bilotta & Brazier, ; Davies‐Colley & Smith, ; Grayson, Finlayson, Gippel, & Hart, ; Judy et al, ; Longstaff & Dennison, ). As such, turbidity is an often‐measured, monitored, and regulated water quality parameter in aquatic systems around the world (Z. Lee et al, ).…”

Section: Introductionmentioning

confidence: 99%

Monitoring turbidity from above: Deploying small unoccupied aerial vehicles to image in‐stream turbidity

Ehmann

Kelleher

Condon

2019

Hydrological Processes

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Small unoccupied aerial systems (sUASs) are increasingly applied to study hydrologic processes and water quality. Here, we evaluate a novel application of sUAS to stream turbidity monitoring, with the goal of extending analyses implemented with satellite remote sensing to enable high resolution, rapid collection of turbidity imagery along smaller waterbodies. To accomplish this, we collected multispectral imagery using two sUAS platforms under a range of environmental conditions along a local creek in Syracuse, NY. In addition, we collected in situ turbidity observations immediately after each flight along several transects along the creek, as well as within a clear plume created by a natural spring entering the main channel of the creek. The in situ turbidity values were compared with the mean and standard deviation of several single‐band and multiband indices extracted along similar transects from the sUAS flights. On the basis of data collected across several flights, we found optical metrics obtained from multispectral imagery correlated well with in situ turbidity measurements. Though many optical metrics yielded strong relationships considering only values within the main channel, values associated with the red band were strongly related to turbidity estimates from the main channel as well as lower turbidity values observed in the spring plume. Although there are still limitations of this approach associated with variable field conditions, results from this proof of concept analysis show that sUASs offer a promising avenue for cost‐effective turbidity monitoring.

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Lab on a Secchi disk: A prototype open‐source profiling package for low‐cost monitoring in aquatic environments

Brewin,

Bresnahan

et al. 2024

Limnology & Ocean Methods

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Owing to the high cost of commercial optical sensors, there is a need to develop low‐cost optical sensing packages to expand monitoring of aquatic environments, particularly in under‐resourced regions. Visual methods to monitor the optical properties of water, like the Secchi disk and Forel‐Ule color scale, remain in use in the modern era owing to their simplicity, low‐cost and long history of use. Yet, recent years have seen advances in low‐cost, electronic‐based optical sensing. Here, the designs of a miniaturized hand‐held device (mini‐Secchi disk) that measures the Secchi depth and Forel‐Ule color are updated. We then extend the device by integrating a small electronic sensing package (Arduino‐based) into the Secchi disk, for vertical profiling, combining historic and modern methods for monitoring the optical properties of water into a single, low‐cost sensing device, that measures positioning (GPS), light spectra, temperature, and pressure. It is charged and transfers data wirelessly, is encased in epoxy resin, and can be used to derive vertical profiles of spectral light attenuation and temperature, in addition to Secchi depth and Forel‐Ule color. We present data from a series of deployments of the package, compare its performance with commercially available instruments, and demonstrate its use for validation of satellite remotely sensed data. Our designs are made openly available to promote community‐based development and have potential in communicating and teaching science, participatory science, and low‐cost monitoring of aquatic environments.

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Global Water Clarity: Continuing a Century-Long Monitoring

Abstract: An approach that combines field observations and satellite inferences of Secchi depth could transform how we assess water clarity across the globe and pinpoint key changes over the past century.

Cited by 20 publications

References 1 publication

Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

Monitoring turbidity from above: Deploying small unoccupied aerial vehicles to image in‐stream turbidity

Lab on a Secchi disk: A prototype open‐source profiling package for low‐cost monitoring in aquatic environments

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