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Ashok K. Mishra and Vijay P. Singh

in Texas considering PHDI as the hydrological drought index and precipitation and temperature as meteorological variables. The length of burn-in period is 10 000 and the number of iteration was chosen as 50 000 during the sampling process of the Bayesian regression analysis. The simulation of PHDI was carried out considering 1900–55 as training period and 1956–2000 as testing period ( Figure 3 ). It was observed that the wavelet–Bayesian regression approach was able to better match the pattern

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Di Long, Bridget R. Scanlon, D. Nelun Fernando, Lei Meng, and Steven M. Quiring

during the growing season ( Guttman 1999 ; Quiring 2009 ). A positive SPI value indicates greater than median precipitation, whereas a negative value indicates less than median precipitation. Ordinary least squares regression (OLSR) with 95% or 90% confidence intervals (both were tested but only 95% confidence intervals are shown in the figures) was used for trend analysis for the time series of climate extreme indices. The nonparametric Kendall’s tau-based slope estimator was also used and showed

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Daniel J. McEvoy, Justin L. Huntington, John T. Abatzoglou, and Laura M. Edwards

streamflow gauges (black stars). 3. Data and methodology 3.1. Climate data Monthly gridded datasets of total P and maximum and minimum T at 4-km spatial resolution from the Parameter-Elevation Regression on Independent Slopes Model (PRISM; Daly et al. 1994 ) were used in this analysis with a period of record (POR) from 1895 to 2010. PRISM incorporates orographic effects on P and T inversions that are common in valley floor areas. It has been shown to outperform other available long-term gridded

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