Estimating Palmer Drought Severity Index using a wavelet fuzzy logic model based on meteorological variables

The Palmer Drought Severity Index (PDSI) is widely used to characterize droughts. The PDSI is based on the water balance equation over an area of concern. Calculating PDSI requires data on precipitation, temperature, soil moisture, and the previous PDSI value. While precipitation and temperature time series data are easily available for most locations, it is not always the case with soil moisture due to the lack of soil-moisture monitoring networks. This study developed a wavelet fuzzy logic model (WFL) to overcome the problem. The proposed model employs commonly available precipitation, temperature, and large-scale climate indices as predictors and PDSI as a predictand. The WFL model is applied to ten climate divisions in Texas and its performance is compared with conventional fuzzy logic (FL) model performance. It is shown that the WFL model outperforms the FL model. The variation of WFL model performance along with the average wavelet spectra of precipitation time series is evaluated. Results show that the WFL model is capable of predicting PDSI.