[Abstract]

High-resolution gridded data sets provide valuable opportunities to enhance drought forecasting, but applying complex machine learning algorithms across large spatial domains is computationally challenging. This study presents a novel hybrid approach for forecasting the gridded Standardized Precipitation-Evapotranspiration Index (SPEI) across the U.S. Southern Plains (SP), with lead times of 1 and 3 months. We developed a clustering-based method using 21 centroid grid cells, each representing a unique cluster of similar grid cells based on various hydrologic characteristics, to train and evaluate multilayer perceptrons (MLPs), long short-term memory (LSTM), and genetic programming (GP). Based on the superior performance of the trained MLPs in terms of Nash-Sutcliffe efficiency and root-mean-square error, they were extended to corresponding grid cells for each cluster, enabling spatially adaptive drought prediction at a high resolution. The use of discrete wavelet transform (DWT) further enhanced model accuracy by capturing key temporal patterns in the SPEI series. Notably, our results showed that physical and hydrologic attributes strongly influenced input selections. While a 12-month lag period worked well in regions with weaker seasonality, areas with strong seasonality benefited from selection of effective lags by using mutual information. For 3-month-ahead forecasts, including decomposed potential evapotranspiration in addition to precipitation as inputs improved accuracy in drier regions but decreased accuracy in humid areas. The forecast maps based on the hybrid DWT-MLP models effectively captured the spatial variability of drought, with high correlations to observed values, demonstrating their effectiveness for regional drought early warning systems to inform water resources management adaptations.

[Article Information]

 - Source title:  Water Resources Research, 61(11), e2024WR039744

- DOI: 10.1029/2024WR039744

[Author PURE profile]

 Assistant Professor SangHyun Lee

- Department of Rural Construction Engineering