The [FLARE project](Forecasting Lake and Reservoir Ecosystems)(https://flare-forecast.org/) creates open-source software for flexible, scalable, robust, and near-real time iterative ecological forecasts in lakes and reservoirs. It uses data assimilation to update the initial starting point for a forecast and the model parameters based a real-time statistical comparisons to observations. It has been developed, tested, and evaluated for Falling Creek Reservoir in Vinton, VA (Thomas et al. 2020).

FLAREr is a set of R scripts that

• Generating the inputs and configuration files required by the General Lake Model (GLM)
• Applying data assimilation to GLM
• Processing and archiving forecast output
• Visualizing forecast output

FLARE uses the 1-D General Lake Model (Hipsey et al. 2019) as the mechanistic process model that predicts hydrodynamics of the lake or reservoir. For forecasts of water quality, it uses GLM with the Aquatic Ecosystem Dynamics library. The binaries for GLM and GLM-AED are included in the FLARE code that is available on GitHub. FLARE requires GLM version 3.1 or higher.

More information about the GLM can be found here:

• GLM users guide
• GLM 3.0.0 manuscript
• GLM on GitHub
• AED on GitHub

FLARE development has been supported by grants from the U.S. National Science Foundation (CNS-1737424, DBI-1933016, DBI-1933102)

You will need to download the necessary packages prior to running.

remotes::install_github("FLARE-forecast/GLM3r")
remotes::install_github("FLARE-forecast/FLAREr")

FLAREr is a set of functions that address key steps in the forecasting workflow.

User generated in situ observations, meteorology, and inflow/outflow in a specified format. See FLARE example vignette for format specification.

create_glm_inflow_outflow_files(): generates inflow and output files in GLM format.
create_obs_matrix(): generate the matrix of observations require in data assimilation. generate_glm_met_files(): generates meteorology inputs (both past and future) in the GLM format.
generate_initial_conditions(): generates initial condition for data assimilation and forecasting.
initiate_model_error(): generates the standard deviations for the model error.

run_da_forecast(): runs data assimilation and forecasting.

write_forecast_netcdf(): write output in Ecological Forecasting Initiative standards.
create_flare_metadata(): write metadata in Ecological Forecasting Initiative standards.
plotting_general(): generates a PDF with default visualizations.