A Place‐Based Approach to Drought Forecasting in South‐Central Oklahoma

Abstract

To assist water managers in south‐central Oklahoma prepare for future drought, reliable place‐based drought forecasts are produced. Past‐, present‐, and future‐forecasted climate indices (Multivariate ENSO Index, Pacific Decadal Oscillation index, and Atlantic Multidecadal Oscillation index) and past and present Palmer Drought Severity Index (PDSI) are employed as predictor variables to forecast PDSI using a multivariate regression technique. PDSI is forecasted 18 months in advance with sufficient skill to provide water managers early warning of drought. Using a training data set obtained from the period January 1901 to November 2021, a second‐order model equation that contains, without any restriction, all the predictors and their interaction terms is built to predict drought intensity. Significant predictors are selected through stepwise regression, with cross‐validation producing the simplest restricted model that describes the data well. PDSI values are predicted using 1000 fitted restricted models produced from bootstrapping, then averaged monthly. The technique found the best‐fitting model and estimated the model coefficients that minimized the sum of squared deviations between the fitted model and the predictor variables. The adjusted R‐squared value of the restricted model is large enough to explain an adequately accurate model, and relatively low values of error measures point to good predictive ability of the model. Although the model slightly overestimates the PDSI forecast maxima and minima, it necessarily captures the timing of the periods of severe to exceptional drought.