Drought Prediction for Areas with Sparse Monitoring Networks: A Case Study for Fiji
In one sentence. Extra-Trees and AdaBoost correct seasonal forecasts and WRF downscaling to predict 6-month drought (SPI) in data-sparse Fiji, outperforming bias-corrected forecasts.
Abstract
Hybrid drought prediction models were developed for areas with limited monitoring gauges using the APEC Climate Center Multi-Model Ensemble seasonal climate forecast and machine learning models of Extra-Trees and Adaboost. The models provide spatially distributed detailed drought prediction data of the 6-month Standardized Precipitation Index for the case study area, Fiji. In order to overcome the limitation of a sparse monitoring network, both in-situ data and bias-corrected dynamic downscaling of historical climate data from the Weather Research Forecasting (WRF) model were used as reference data. Performance measures of the mean absolute error as well as classification accuracy were used. The WRF outputs reflect the topography of the area. Hybrid models showed better performance than simply bias corrected forecasts in most cases. Especially, the model based on Extra-Trees trained using the WRF model outputs performed the best in most cases.
Labels
- Topic
- Post-processing & calibration, Sub-seasonal to seasonal
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.3390/w10060788 OpenAlex:W2808053342
- Added
- 2026-10-22
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