- Improving water level forecasts in Lake Champlain basin: skill assessment of operational atmospheric models
Mentors: Dima Beletsky (CIGLR, firstname.lastname@example.org), Daniel Titze (CIGLR), James Kessler (NOAA GLERL)
Project 1 Information:
- What is the accuracy of overland and overlake forcings generated by operational atmospheric models that drive hydrologic, hydrodynamic, and wave models in Lake Champlain basin?
- How does water level forecast accuracy compare when generated by different forcings?
Project Activities: Meteorological forcings that drive WRF-Hydro, WAVEWATCH III and FVCOM models are produced by the following operational atmospheric models: GFS (Global Forecast System) model, HRRR (High Resolution Rapid Refresh) model and its latest update HRRRx. Modeled meteorological variables will be extracted and compared with archived observations. In addition to comparing the skill of the forcing itself, FVCOM and WRF-Hydro model output and skill from runs with the various forcings will be compared.
- Assess the skill of overland and overlake forcings by comparing atmospheric model output with observations of wind, precipitation, air temperature, humidity, etc.
- Compare hydrologic and lake model outputs produced with different meteorological forcings.
- Compare hydrologic and lake model skill produced with different forcings using observations at stream gages and lake level gages.
Required & Desired Skills: Candidates should have strong scientific and computing skills, and experience with data analysis (e.g., using Python, R, IDL). Knowledge of the UNIX/LINUX environment and background in meteorology, hydrology or oceanography is desired.
- Identifying patterns in Great Lakes environmental data collected by an underwater glider
Mentors: Michael Fraker (CIGLR, email@example.com), Russ Miller (CIGLR)
Project 2 Information:
Research Questions: How much variation in the spatial patterns of key ecosystem variables do we see over a season, between years, and between lakes? Are patterns correlated with variation in key environmental drivers (e.g., precipitation, wind direction, spring warming rate) or other local or regional drivers?
Project Activities: The fellow will become familiar with the data being collected and how it may be useful to the study of Great Lakes ecosystem dynamics. S/he will identify patterns in the data using approaches for analyzing large spatial and temporal datasets (e.g., random forest and gradient forest analysis, regression).
- Become familiar with the types of data available and how they may be used
- Use various spatial statistics to identify patterns
Required & Desired Skills: Ideally, the fellow will have an interest in analyzing large datasets and experience using the statistical package R. Knowledge of specific statistical approaches is useful, but not required. A familiarity with aquatic ecology also would be useful, but is not required.
Environmental mechanisms influencing seasonal progression of phytoplankton in the western basin of Lake Erie
Mentors: Reagan M. Errera (NOAA GLERL, firstname.lastname@example.org), Jim Hood (Ohio State University)
Project 3 Information:
- What is the seasonal progression of phytoplankton in the western basin of Lake Erie from April – Oct 2019; Do phytoplankton counts relate to FluoroProbe readings and phytoplankton group distinctions?
- Which environmental factors are correlated to shifts in phytoplankton species dominance? Do shifts in the community composition of primary producers translate into community shifts in higher trophic levels (i.e., zooplankton)?
- Can fluctuations and factors identified in 2019 phytoplankton succession provide additional understanding of bloom dynamics since 2015?
- Identify key time periods within the phytoplankton assemblage composition when population demographics deviate based on phytoplankton composition and biomass.
- Analyze phytoplankton counts provided by BSA Environmental Services, Inc. from Lake Erie in 2019.
- Compare results to FluoroProbe readings and determine calibration
- Identify environmental mechanisms related to phytoplankton population shifts and response strength.
- Correlate environmental factors to phytoplankton community changes through both univariate and multivariate statistical
- Perform a retrospective analysis based on mechanistic drivers identified in 2019 and to Fluoroprobe data collected between 2015 -2018 (based on findings in Activity 1).
Required & Desired Skills: An understanding of aquatic ecology, with a basic understanding of phytoplankton groups and factors influencing growth characteristics and population dynamics. General understanding of statistics (experience with multivariate statistics desired but not required) and experience with statistical programs (such as R, Python, or Matlab).
- Estimating Bythotrephes abundance and potential impacts by three sampling methods: MOCNESS, laser optical counter, and standard zooplankton net
Mentors: Henry Vanderploeg (NOAA GLERL, email@example.com) and Edward Rutherford (NOAA GLERL)
Project 4 Information:
- What is the vertical distribution, abundance, size (biomass), and reproductive status of Bythotrephes determined by the MOCNESS?
- How does estimated abundance and biomass of Bythotrephes sampled by MOCNESS compare with estimates using other sampling methods, and what would these varying estimates imply for our estimates of consumptive impact of zooplankton by Bythotrephes?
Project Activities: We hypothesize that the MOCNESS provides estimates of Bythotrephes abundance and biomass that are much higher than what was collected by standard net tows, and that using MOCNESS has the further advantage of allowing direct comparison with other zooplankton predators (larval fish and Mysis) collected in the same samples.
For this project, the fellow will:
- Examine archived samples to determine concentration, biomass, and fecundity of Bythotrephes in different depth zones sampled by MOCNESS.
- Compare estimates determined by MOCNESS with other sampling methods.
- Participate on at least one 5-day research cruise to sample zooplankton, larval fishes, Mysis, and Bythotrephes using different sampling technologies, and as time allows examine samples collected this field season.
- Work with mentors to determine the consequences of the new estimates to evaluate consumptive impact of Bythotrephes by application of a bioenergetics model.
Required & Desired Skills: Ideal candidates should be familiar with zooplankton and limnology or biological oceanography. A solid background in statistical analysis is necessary and working in R is desirable. Experience on large lake or oceanographic cruises is useful but not required.
Evaluation of next generation runoff risk tools combining process-based and statistical models
Mentors: Brent Lofgren (NOAA GLERL, firstname.lastname@example.org), Lacey Mason (NOAA GLERL), Yao Hu (University of Delaware), Lindsay Fitzpatrick (CIGLR)
Project 5 Information:
- How well does the NOAA National Water Model (NWM) directly simulate runoff at the edge-of-field scale?
- How much is this improved by using a statistical procedure to use specific NWM outputs to predict edge-of-field runoff?
- Interact with other project members to acquire modeled output and “ground truth” data at edge of field locations.
- Conduct an analysis to evaluate and compare the skill of the two modeling frameworks (National Water Model fluxes vs. statistical postprocessing of National Water Model output) at predicting the risk of runoff at edge of field locations.
- Provide recommendations to the team in the form of a final presentation, including graphics summarizing model skill and the intercomparison results.
- Attend bi-weekly project meetings and present progress.
Required & desired skills: Ability to conduct data analysis and, in cooperation with team members, interpret the results to assess the suitability of different methods. A background in hydrologic modeling and ability to code in scripting languages such as Python, Matlab, or R is desired.
- Defining bottlenecks to fish larvae growth, survival, and potential recruitment in Lake Michigan
Mentors: Ed Rutherford (NOAA GLERL, email@example.com), Doran Mason (NOAA GLERL), and Henry Vanderploeg (NOAA GLERL)
Project 6 Information:
- What is the relative importance of Dreissena mussel veligers in the diets of larval fishes?
- Can a diet of veligers support the historic growth and production of larval fishes in the Great Lakes?
- Help conduct surveys and laboratory analysis to learn how density, availability, and energetic content of Dreissena veligers affect diet and growth of larval fish (yellow perch, alewife, bloater).
- Supplement data analysis of new samples collected during diel surveys in Lake Michigan in 2020 with analysis of prior plankton collections in Lake Michigan in 2019.
- Use data from the field surveys and lab analysis to configure and run bioenergetics models of larval fish growth to understand whether consumption of invasive mussel veligers or changes in lake temperature have affected larval growth rates.
Required & Desired Skills: A strong background in freshwater or marine science and completion of an introductory statistics course are required. Experience working on boats in variable weather conditions is desired.
- Connection between Great Lakes and Arctic Ice Cover in Response to Teleconnection Patterns
Mentors: Jia Wang (NOAA GLERL, firstname.lastname@example.org), Ayumi Fujisaki-Manome (CIGLR), Philip Chu (NOAA GLERL), Yoyo Lin (CIGLR)
Project 7 Information:
- In addition to local meteorology, is Great Lakes ice cover correlated to Arctic sea ice cover in response to the same teleconnection patterns?
- If so, how strong are these correlations and are they linear or non-linear?
- Can we hindcast and predict Great Lakes and Arctic ice cover using climate indices?
Project Activities: The fellow will participate in research to investigate the relationship among atmospheric teleconnection patterns and lake/sea ice cover in the Great lakes and the Arctic Ocean (or in their subareas). The predominant relationship identified in this project will be used to develop a tool to predict Great Lakes and Arctic ice cover. Specific activities include:
- Investigate the relationships among climate indices, Great Lakes ice cover, and Arctic sea ice cover using data analyses, including time series analysis, correlation, and/or empirical orthogonal function analysis, and regression analysis.
- Identify predominant relationships between ice cover in a Great Lake (or a certain subarea within a lake) and in a region in the Arctic Ocean.
- Identify climate indices that control the above predominant relationship in lake/sea ice cover.
- Develop a tool to forecast ice cover in the Great Lakes and the Arctic Ocean using the relationships identified from the above activities.
Required & Desired Skills: Experience with at least one programming language (e.g., R-software, Fortran, Python) is required. Data analyses and statistics background are desired.
Toward flood resilience in Great lakes coastal regions: numerical modeling of flood risks in different zones under climate change
Mentors: Yi Hong (CIGLR, email@example.com), Eric J. Anderson (NOAA GLERL)
Project 8 Information:
- Can improved understanding of the frequency and intensity of extreme weather events improve our ability to predict coastal flooding?
- How do we couple hydrological and coastal hydrodynamic models to improve flood forecasting?
Project Activities: This project aims to use hydrologic and hydrodynamic models (FVCOM, WRF-Hydro) to evaluate coastal inundation in the Great Lakes. The summer fellow will use existing models to explore a series of hydro-meteorological scenarios for assessing the flood risks of different coastal regions. Project tasks may include:
- Collecting and analyzing data for model configuration;
- Implementation of the model with different simulation scenarios;
- Extracting and analyzing the model outputs.
Required & Desired Skills: We are looking for a highly motivated graduate/undergraduate student with interest in hydrology, hydrodynamics, environmental science, and numerical modeling. The ideal candidate will have or be working toward a degree in an appropriate discipline (such as Environmental Science, Engineering, etc.) with strong skills in data analysis and computer science.
- Climatic influences on antipredator phenotypic plasticity in larval amphibians
Mentor: Michael Fraker (CIGLR, firstname.lastname@example.org)
Project 9 Information:
- Using existing samples of tadpoles and predatory invertebrates from a 20-year biannual survey of 39 pond communities, how do the morphological phenotypes of 6 species of tadpoles vary between relatively wet years (e.g., 2002) and drought years (e.g., 2007)?
- Which abiotic and biotic variables are common drivers among species?
Project Activities: All individuals (tadpoles and invertebrates) from each sample year will be digitally photographed, then morphological characteristics will be measured. The morphological measurements will be combined with data on the abundance of all pond species, pond characteristics (e.g., hydroperiod, canopy cover), and meteorological data, then analyzed using multivariate approaches. The project will focus on the wettest and driest years first, then expand to other years as time permits.
- Digitize samples
- Measure morphology of all individuals using ImageJ
- Use multivariate statistical approaches (e.g., CCA, random forest) to identify patterns and drivers of tadpole phenotypes
Required & Desired Skills: Ideally, the fellow will have an interest and background in aquatic community ecology and climate change. Experience using the statistical package R also is useful, but not required.