Extending SC-PDSI-PM with neural network regression using GLDAS data and Permutation Feature Importance
In one sentence. Neural network regression extends the global PDSI drought index from GLDAS data, achieving RMSE below 1 over 99.5% of the globe and providing an updated dataset.
Abstract
The Palmer Drought Severity Index (PDSI) ranges from −10 to 10 and is used for monitoring drought extent and severity. PDSI is a monthly global gridded data set with partial global coverage from 1850 through 1947 and full global coverage from 1948 through 2018. PDSI updates are infrequent. We present a method to extend PDSI using Global Land Data Assimilation System (GLDAS) data. We provide an updated dataset and code for the method. We discuss the accuracy and bias of the method for various regions. We have high accuracy, with 99.5% of the globe exhibiting RMSE values less than 1. Globally our method is unbiased with an average ME of approximately 0. Some regions have slight biases with dryer and wetter regions showing a slight negative and positive biases, respectively. Prediction errors exhibits spatial trends with the highest errors in areas with extreme climate.
Labels
- Topic
- Extremes & hazards
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1016/j.envsoft.2022.105475 OpenAlex:W4293733620
- Added
- 2026-10-22
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