McPherson Renee A, Corporal-Lodangco Irenea L, Richman Michael B
South Central Climate Adaptation Science Center University of Oklahoma Norman OK USA.
Department of Geography and Environmental Sustainability University of Oklahoma Norman OK USA.
Earth Space Sci. 2022 Oct;9(10):e2022EA002315. doi: 10.1029/2022EA002315. Epub 2022 Oct 25.
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.
为帮助俄克拉荷马州中南部的水资源管理者为未来干旱做好准备,制作了可靠的基于地点的干旱预测。过去、当前和未来预测的气候指数(多变量厄尔尼诺指数、太平洋年代际振荡指数和大西洋多年代际振荡指数)以及过去和当前的帕尔默干旱严重指数(PDSI)被用作预测变量,采用多元回归技术预测PDSI。PDSI提前18个月进行预测,具有足够的技能为水资源管理者提供干旱预警。使用从1901年1月至2021年11月期间获得的训练数据集,构建了一个二阶模型方程,该方程不受任何限制地包含所有预测变量及其交互项,以预测干旱强度。通过逐步回归选择显著的预测变量,交叉验证产生了能很好描述数据的最简单受限模型。使用从自抽样产生的1000个拟合受限模型预测PDSI值,然后按月平均。该技术找到了最佳拟合模型,并估计了使拟合模型与预测变量之间的平方偏差之和最小化的模型系数。受限模型的调整后R平方值足够大,足以解释一个足够准确的模型,并且误差度量的相对较低值表明该模型具有良好的预测能力。尽管该模型略微高估了PDSI预测的最大值和最小值,但它必然捕捉到了严重到异常干旱时期的时间。