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Browsing by Author "Hewett, Russell J."

Now showing 1 - 3 of 3
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    Dynamics of the stream-lake transitional zone affect littoral lake metabolism
    Ward, Nicole K.; Brentrup, Jennifer A.; Richardson, David C.; Weathers, Kathleen C.; Hanson, Paul C.; Hewett, Russell J.; Carey, Cayelan C. (Springer, 2022-07)
    Lake ecosystems, as integrators of watershed and climate stressors, are sentinels of change. However, there is an inherent time-lag between stressors and whole-lake response. Aquatic metabolism, including gross primary production (GPP) and respiration (R), of stream-lake transitional zones may bridge the time-lag of lake response to allochthonous inputs. In this study, we used high-frequency dissolved oxygen data and inverse modeling to estimate daily rates of summer epilimnetic GPP and R in a nutrient-limited oligotrophic lake at two littoral sites located near different major inflows and at a pelagic site. We examined the relative importance of stream variables in comparison to meteorological and in-lake predictors of GPP and R. One of the inflow streams was substantially warmer than the other and primarily entered the lake's epilimnion, whereas the colder stream primarily mixed into the metalimnion or hypolimnion. Maximum GPP and R rates were 0.2-2.5 mg O-2 L-1 day(-1) (9-670%) higher at littoral sites than the pelagic site. Ensemble machine learning analyses revealed that > 30% of variability in daily littoral zone GPP and R was attributable to stream depth and stream-lake transitional zone mixing metrics. The warm-stream inflow likely stimulated littoral GPP and R, while the cold-stream inflow only stimulated littoral zone GPP and R when mixing with the epilimnion. The higher GPP and R observed near inflows in our study may provide a sentinel-of-the-sentinel signal, bridging the time-lag between stream inputs and in-lake processing, enabling an earlier indication of whole-lake response to upstream stressors.
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    The SunPy Project: Open Source Development and Status of the Version 1.0 Core Package
    Barnes, Will T.; Bobra, Monica G.; Christe, Steven D.; Freij, Nabil; Hayes, Laura A.; Ireland, Jack; Mumford, Stuart J.; Perez-Suarez, David; Ryan, Daniel F.; Shih, Albert Y.; Chanda, Prateek; Glogowski, Kolja; Hewett, Russell J.; Hughitt, V. Keith; Hill, Andrew; Hiware, Kaustubh; Inglis, Andrew; Kirk, Michael S. F.; Konge, Sudarshan; Mason, James Paul; Maloney, Shane Anthony; Murray, Sophie A.; Panda, Asish; Park, Jongyeob; Pereira, Tiago M. D.; Reardon, Kevin; Savage, Sabrina; Sipocz, Brigitta M.; Stansby, David; Jain, Yash; Taylor, Garrison; Yadav, Tannmay; Rajul; Dang, Trung Kien (2020-02-12)
    The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project.
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    A Survey of Computational Tools in Solar Physics
    Bobra, Monica G.; Mumford, Stuart J.; Hewett, Russell J.; Christe, Steven D.; Reardon, Kevin; Savage, Sabrina; Ireland, Jack; Pereira, Tiago M. D.; Chen, Bin; Perez-Suarez, David (2020-04-20)
    The SunPy Project developed a 13-question survey to understand the software and hardware usage of the solar-physics community. Of the solar-physics community, 364 members across 35 countries responded to our survey. We found that 99 +/- 0.5 of respondents use software in their research and 66% use the Python scientific-software stack. Students are twice as likely as faculty, staff scientists, and researchers to use Python rather than Interactive Data Language (IDL). In this respect, the astrophysics and solar-physics communities differ widely: 78% of solar-physics faculty, staff scientists, and researchers in our sample uses IDL, compared with 44% of astrophysics faculty and scientists sampled by Momcheva and Tollerud (2015). 63 +/- 4 of respondents have not taken any computer-science courses at an undergraduate or graduate level. We also found that most respondents use consumer hardware to run software for solar-physics research. Although 82% of respondents work with data from space-based or ground-based missions, some of which (e.g. the Solar Dynamics Observatory and Daniel K. Inouye Solar Telescope) produce terabytes of data a day, 14% use a regional or national cluster, 5% use a commercial cloud provider, and 29% use exclusively a laptop or desktop. Finally, we found that 73 +/- 4 of respondents cite scientific software in their research, although only 42 +/- 3 do so routinely.