Corrected surface water temperatures using Random Forests and process-based hydrodynamic models
0
Description
This dataset provides corrected predictions of surface water temperature of Lake Harsha (Ohio, US) for year 2019. Corrected predictions are provided by a hybrid modeling approach that combines (a) a process-based hydrodynamic model of the lake and (b) a Random Forest model that predicts errors of the process-based models. The Random Forest model was trained on simulation errors provided by the process-based model during the period 2015-2018.
Parameter: Surface water temperature (oC )
Spatial resolution | Temporal resolution |
60 m | Daily |
Temporal coverage
2015 – 2019
Links
Area of interest
Leave a Reply Cancel reply
You must be logged in to post a comment.
Contact info
Related Datasets
- Hydrological forecasted variables from HYPE model
- WW-HYPE simulated data of particulate phoshporus concentrations in outflow from subbasin (William H Harsha lake)
- Discrete In situ physicochemical data for William H Harsha Lake (grab samples)
- Chlorophyll-a measurements from USACE/USEPA (subsets)
- Water balance data for William H Harsha Lake
- EO-derived total suspended matter depth for William H Harsha Lake using Sentinel 2