Margaux Sleckman
Margaux (she/her) is a data scientist within the Data Science Branch, housed in the Integrated Information Disseminiation Division of the of the USGS Water Mission Area.
Margaux has a background in Environmental Data Science, Spatial Data Analysis, and Environmental Science more broadly. Prior to USGS, Margaux worked as a sustainability consultant in the Netherlands, where she focused on developing data science and spatial analysis methodologies to guide environmental impact mitigation and adaptation efforts for land-intensive agrifood companies.
Margaux has a Masters (MESM) degree from the Bren School of Environmental Science and Management at UC Santa Barbara, where she honed her environmental data science and problem-solving skills. There, she completed a master’s thesis in collaboration with The Nature Conservancy that sought to understand and model the driving factors of low ecological impact wind development.
Professional Experience
2019 – 2021 Sustainability Consultant – Data Science, Metabolic B.V.
2017 – 2018 Scientific Computing Intern, National Center for Ecological Analysis & Synthesis, Santa Barbara
2016 – 2017 Americorps Fellow – San Francisco Department of the Environment (SFE)
Education and Certifications
MESM, Environmental Science & Management, 2019. Bren School of the University of California, Santa Barbara
Certificate, Geographic Information Systems, 2017. City College of San Francisco
B.A. Environment – Environment Economics & International Development, 2016. McGill University
Honors and Awards
USGS Star Award (annual) - 2022
Science and Products
Model Code, Outputs, and Supporting Data for Approaches to Process-Guided Deep Learning for Groundwater-Influenced Stream Temperature Predictions
This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately incorporate groundwater-discharge processes. We assessed the performance of an existing process-guided deep learning stream temperature model of the Delaware River Basin (USA) and explored four approaches fo
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin
Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish species. This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper DRB. The modeling approach in
Data to support water quality modeling efforts in the Delaware River Basin
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this data release
Predictive understanding of stream salinization in a developed watershed using machine learning
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. We comp
Authors
Jared David Smith, Lauren Elizabeth Koenig, Margaux Jeanne Sleckman, Alison P. Appling, Jeffrey M Sadler, Vincent T. DePaul, Zoltan Szabo
Train, inform, borrow, or combine? Approaches to process-guided deep learning for groundwater-influenced stream temperature prediction
Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River B
Authors
Janet R. Barclay, Simon Nemer Topp, Lauren Elizabeth Koenig, Margaux Jeanne Sleckman, Alison P. Appling
Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations
Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations
Authors
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White
Science and Products
Model Code, Outputs, and Supporting Data for Approaches to Process-Guided Deep Learning for Groundwater-Influenced Stream Temperature Predictions
This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately incorporate groundwater-discharge processes. We assessed the performance of an existing process-guided deep learning stream temperature model of the Delaware River Basin (USA) and explored four approaches fo
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin
Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish species. This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper DRB. The modeling approach in
Data to support water quality modeling efforts in the Delaware River Basin
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this data release
Predictive understanding of stream salinization in a developed watershed using machine learning
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. We comp
Authors
Jared David Smith, Lauren Elizabeth Koenig, Margaux Jeanne Sleckman, Alison P. Appling, Jeffrey M Sadler, Vincent T. DePaul, Zoltan Szabo
Train, inform, borrow, or combine? Approaches to process-guided deep learning for groundwater-influenced stream temperature prediction
Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River B
Authors
Janet R. Barclay, Simon Nemer Topp, Lauren Elizabeth Koenig, Margaux Jeanne Sleckman, Alison P. Appling
Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations
Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations
Authors
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White