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and Hydrology","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\u003c!-- badges: start --\u003e\n[![R-CMD-check](https://github.com/FLARE-forecast/FLAREr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/FLARE-forecast/FLAREr/actions/workflows/R-CMD-check.yaml)\n[![Codecov test coverage](https://codecov.io/gh/FLARE-forecast/FLAREr/branch/main/graph/badge.svg)](https://codecov.io/gh/FLARE-forecast/FLAREr?branch=main)\n\u003c!-- badges: end --\u003e\n\n# FLAREr\n\nThis document serves as a user guide and a tutorial for the FLARE (Forecasting Lake and Reservoir Ecosystems) system ([Thomas et al. 2020](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR026138)). FLARE generates forecasts with uncertainty of water temperature and water quality for 1- to 35-day-ahead time horizon at multiple depths of a lake or reservoir. 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 Virginia ([Thomas et al. 2020](https://doi.org/10.1029/2019WR026138)), Beaverdam Reservoir in Virginia ([Wander et al. 2024](https://doi.org/10.1002/ecs2.4752)), Lake Sunapee in New Hampshire ([Woelmer et al. 2024](https://doi.org/10.1016/j.ecoinf.2024.102825)), National Ecological Observatory Network lakes across the United States ([Thomas et al. 2023](https://doi.org/10.1002/fee.2623) and [Olsson et al. 2024](https://doi.org/10.1029/2023WR035901)), and Lough Feeagh in Ireland ([Páiz et al. 2025](https://doi.org/10.1002/ecs2.70335)). FLARE was among the top-performing forecast models in a year-long water quality forecasting challenge across seven lakes ([Olsson et al. 2025](https://doi.org/10.1002/eap.70004)).\n\nFLAREr is a set of R scripts that\n\n* Generating the inputs and configuration files required by the General Lake Model (GLM)\n* Applying data assimilation to GLM\n* Processing and archiving forecast output\n* Visualizing forecast output\n\nFLARE uses the 1-D General Lake Model ([Hipsey et al. 2019](https://www.geosci-model-dev.net/12/473/2019/)) 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. FLARE v4.0 requires GLM-AED version 4, which adds the NetCDF restart capacity that FLARE's restart workflow depends on. GLM-AED 4 is currently available on the `v4alpha` branch of GLM-AED and is provided by the `GLMAEDr` package (see [Installation](#installation) below).\n\nMore information about the GLM can be found here:\n\n* [GLM 3.0.0 manuscript](https://www.geosci-model-dev.net/12/473/2019/) \n* [GLM on GitHub](https://github.com/AquaticEcoDynamics/glm-aed)\n* [GLM users guide](https://aquaticecodynamics.github.io/glm-workbook/) \n\nFLARE development has been supported by grants from the U.S. National Science Foundation (CNS-1737424, DBI-1933016, DBI-1933102)\n\n## Installation\n\nYou will need to download the necessary packages before running.\n\n```\nremotes::install_github(\"FLARE-forecast/FLAREr\")\n```\n\nNext, you need the GLM model.  You can get in using multiple pathways\n\nThe recommended way is to install the `GLMAEDr` package from Github, which downloads and manages the GLM binary for you. It provides GLM-AED version 4 (the `v4alpha` build), which FLARE v4.0 requires for its NetCDF restart capacity:\n\n```\nremotes::install_github(\"flare-forecast/GLMAEDr\")\nGLMAEDr::glm_install()\nSys.setenv('GLM_PATH'=GLMAEDr::glm_path())\n```\n\n\u003e **Windows is not currently supported.** GLM-AED version 4 binaries are not yet built for Windows, so `GLMAEDr` cannot provide a working GLM binary on that platform. Use macOS or Linux (for example, via WSL2 on Windows) until v4 Windows binaries are available.\n\nThe alternative pathways below predate GLM-AED 4 and may not include the NetCDF restart capacity. Use them only if the binary they provide is a GLM-AED 4 (`v4alpha`) build; otherwise the FLARE restart workflow will not work.\n\nAlternatively, you can install the `GLM3r` package from Github using\n\n```\nremotes::install_github(\"rqthomas/GLM3r\")\n```\n\nor you can download it from the AquaticEcoDynamics GitHub organization.  This code assumes you are in the directory with the FLARE configurations and workflow subdirectories\n\n```\ndownload.file(\"https://github.com/rqthomas/glm-aed/archive/refs/heads/main.zip\", \"glm_aed.zip\")\nunzip(\"glm_aed.zip\")\n```\n\nif you are running on Mac you will need to run:\n\n```\nsystem2(\"chmod\",\"u+x glm-aed-main/binaries/macos/Sonoma/glm_latest/glm\")\nsystem2(\"./glm-aed-main/binaries/macos/Sonoma/glm_latest/glm\")\n```\n\nif you are running on Linux you will need to run:\n\n```\nsystem2(\"chmod\",\"u+x glm-aed-main/binaries/ubuntu/22.04/glm_latest/glm\")\nsystem2(\"./glm-aed-main/binaries/ubuntu/22.04/glm_latest/glm\")\n```\n\n## Use\n\nFLAREr is a set of functions that address key steps in the forecasting workflow. \n\n### Requires\n\nUser-generated *insitu* observations, meteorology, and inflow/outflow in a specified format.  See the FLARE example vignette for format specification.\n\nThe expected top-level directory structure (referred to as `lake_directory` in the code) is:\n\n```\nlake_directory/\n├── configuration/\n│   └── \u003cconfig_set_name\u003e/   # e.g. \"default\" — holds FLARE yml files and GLM nml\n├── drivers/\n│   ├── met/                 # meteorological driver files\n│   ├── iflow/               # inflow driver files\n│   └── oflow/               # outflow driver files\n└── targets/\n    └── \u003csite_id\u003e/           # observed in-situ data\n```\n\nThe `configuration/` sub-directory must contain a `configure_run.yml` file and the GLM namelist (`glm3.nml`). Additional configuration files (states, parameters, observations) live alongside these. See the [FLAREr example vignette](articles/flare-example-vignette.html) for full format specifications.\n\n### Quick Run\n\nThe code below will produce a single forecast for Falling Creek Reservoir using configuration files included with the package.\n\n```\nlibrary(arrow)\nlibrary(tidyverse)\nlibrary(FLAREr)\n\nremotes::install_github(\"flare-forecast/GLMAEDr\")\nGLMAEDr::glm_install()\nSys.setenv('GLM_PATH'=GLMAEDr::glm_path())\n\n\ndir.create(tempdir(),showWarnings = FALSE)\nlake_directory \u003c- file.path(tempdir(), \"extdata\")\nfile.copy(system.file(\"extdata\", package = \"FLAREr\"), tempdir(), recursive = TRUE)\nrun_flare(lake_directory = lake_directory,configure_run_file = \"configure_run.yml\", config_set_name = \"default\")\n\nopen_dataset(file.path(lake_directory,\"forecasts/parquet\")) |\u003e \n  filter(variable == \"temperature\",\n         depth == 1) |\u003e \n  collect() |\u003e \n  ggplot(aes(x = datetime, y = prediction, group = parameter)) +\n  geom_line() +\n  geom_vline(aes(xintercept = as_datetime(reference_datetime))) +\n  labs(title = \"1 m water temperature forecast\")\n```\n\n## Storage backends (local, S3, and FaaSr)\n\nFLAREr reads its drivers, targets, and configuration — and writes its\nforecast output, scores, and restart files — through a single I/O layer\nthat can target three different backends. The backend is selected per run\nfrom two flags in `configure_run.yml`:\n\n| `use_s3` | `use_faasr` | Mode    | Where data lives |\n|----------|-------------|---------|------------------|\n| `FALSE`  | `FALSE`     | `local` | the local filesystem under `lake_directory` |\n| `TRUE`   | `FALSE`     | `s3`    | an S3-compatible bucket, accessed directly with `aws.s3` / `arrow` |\n| `TRUE`   | `TRUE`      | `faasr` | a FaaSr DataStore, accessed through the FaaSr runtime |\n\nWhen neither flag is set both default to `FALSE` (`local` mode), so\nexisting local configurations keep working unchanged. `use_faasr: TRUE`\nrequires `use_s3: TRUE` — FaaSr mode is always cloud-backed and FLAREr\nerrors at startup if `use_faasr` is set without `use_s3`.\n\nBucket names and endpoints for `s3` and `faasr` modes are defined in the\n`s3:` block of `configure_flare.yml`, with one entry per DataStore (e.g.\n`drivers`, `targets`, `forecasts_parquet`, `restart`, `scores`). Set\n`anonymous: true` on a store for public read-only buckets; non-anonymous\naccess reads `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` from the\nenvironment.\n\n### FaaSr (serverless cloud execution)\n\n[FaaSr](https://faasr.io) runs R functions as serverless actions on\nplatforms such as GitHub Actions, AWS Lambda, and OpenWhisk, using\nS3-compatible *DataStores* for persistent storage. When FLAREr runs as a\nFaaSr action, set `use_faasr: TRUE` so that all object I/O is routed\nthrough the FaaSr runtime's helpers (`faasr_get_file()`,\n`faasr_put_file()`, `faasr_arrow_s3_bucket()`, …) instead of calling\n`aws.s3` directly. FaaSr supplies the DataStore credentials, so they do\nnot need to be baked into the FLAREr configuration.\n\nFaaSr is **not** a package dependency of FLAREr: its helper functions are\ninjected into the global environment by the FaaSr executor when an action\nstarts. If `use_faasr: TRUE` but those helpers are not present — for\nexample, when the same configuration is run outside a FaaSr container —\nFLAREr prints a warning and falls back to `s3` mode.\n\nInside a FaaSr action function, call `initialize_faasr(config)` once\nbefore `run_flare()` to validate the configuration: it errors on the\ninconsistent `use_faasr: TRUE` / `use_s3: FALSE` combination and warns\nearly if AWS credentials are missing. 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