Published June 11, 2025
| Version v1
Dataset
Open
Developing scenario-based, near-term iterative forecasts to inform water management: data, forecasts, scores
Creators
-
1.
Virginia Tech
- 2. The University of Western Australia
- 3. Department for Environment and Water, Government of South Australia
Description
This data publication contains zipped parquet files from the Lake Alexandrina scenario-based forecasting work using the FLARE (Forecasting Lake And Reservoir Ecosystems) system and two null models:
- targets.zip contains in-situ water temperature, salinity, and depth observations
- drivers.zip contains NOAA driver forecast files
- forecasts.zip contains forecast parquet files generated from the scenario workflows for the reference (glm_flare_v3_crest), two alternate scenarios (glm_flare_v3_crest_down_0.1and glm_flare_v3_crest_up_0.1), and two null models (climatology and persistenceRW)
- scores.zip cotains forecast skill metrics of the evaluated forecasts.
Instructions on using these data to replicate analyses from the manuscript can be found in the associated code publication.
Files
drivers.zip
Additional details
Related works
- Is derived from
- Software: 10.5281/zenodo.15649750 (DOI)
Funding
- U.S. National Science Foundation
- Global Centers Track 2: Building the Global Center for Forecasting Freshwater Futures OISE-2330211
- U.S. National Science Foundation
- NEON RCN: The Ecological Forecasting Initiative RCN: Using NEON-enabled near-term forecasting to synthesize our understanding of predictability across ecological systems and scales DEB-1926388
- U.S. National Science Foundation
- Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management EF-2318861