Lauren Koenig

Oct 16, 2024 | pubs.usgs.gov | Jared Smith |Lauren Koenig |Margaux Jeanne Sleckman |Alison P. Appling

Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed.

Oct 11, 2024 | dx.doi.org | Jared Smith |Lauren Koenig |Alison P. Appling |Margaux J. Sleckman

Oct 3, 2024 | pubs.usgs.gov | Jeffrey M. Sadler |Lauren Koenig |Galen Gorski |Alice Carter

Links More information: Publisher Index Page (via DOI) Download citation as: RIS | Dublin Core Publication type Article Publication Subtype Journal Article Title Evaluating a process-guided deep learning approach for predicting dissolved oxygen in streams Series title Hydrological Processes DOI 10.1002/hyp.15270 Volume 38 Issue 9 Year Published 2024 Language English Publisher Wiley Contributing office(s) WMA - Integrated Modeling and Prediction Division Description e15270, 13 p. Google...

Sep 19, 2024 | onlinelibrary.wiley.com | Lauren Koenig |Galen Gorski |Alice Carter |Jeffrey M. Sadler

Supporting Information Filename Description hyp15270-sup-0001-Supinfo.pdfPDF document, 3.3 MB Data S1. hyp15270-sup-0002-TableS3.pdfPDF document, 66.7 KB Table S3. Input variables. REFERENCES (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33, 121–131. https://doi.org/10.1002/joc.3413 , & (2021).

Jul 6, 2024 | newsbreak.com | Lauren Koenig |Samantha Waldenberg |Betsy Klein

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