Uncertainty Analysis of Standardized Precipitation Index Due to the Effects of Probability Distributions and Parameter Errors - Zhang, Ying; Li, Zhanling (Frontiers in Earth Science, 2020)
The standardized precipitation index (SPI) is widely used in drought assessments to monitor drought conditions due to its simple data requirement and multi-scale characteristics. However, there are some uncertainties existing in the process of its calculation cannot be ignored for accurate drought assessments. This study, taking the Heihe River basin in northwest of China as the study area, mainly focuses on investigated the uncertainty issuesies both in SPI calculation and in drought characteristics associated with the probability distributions and parameter estimation errors. based on the precipitation data at 4 9 meteorological stations covering a period of 1960-2015 in Heihe River basin(Northwest China). 10Ten probability distributions (two- and three-parameter Log-logistic and Lognormal, Generalized Extreme Value, Pearson-III, Burr, Gamma, Inverse Gaussian and Weibull) are employed were selected to estimate the SPI.and Maximum Likelihood Estimation isMaximum Likelihood EstimationMLE was used to estimate distribution parameters. Randomly generating parameters based on the normality assumption was is applied to quantify the uncertainty of parameter estimations and their effects toeffects on drought index. Results showed that, Log-Logistic logistic type distribution presents quite close performance with the benchmark Gamma distribution, and thus was is recommended as an alternatives in SPI calculation in fitting the precipitation data over the study area., and resulting in large uncertainty in SPI values although it passed the Kolmogorov-SmirnovK-S and Anderson-DarlingA-D tests. Effects of both uncertainty sources grades are more reflected on extreme droughts (extremely dry or wet). The more extreme the SPI value, the greater uncertainties caused bythese two both sources. Furthermore, the drought characteristics vary a lot from different distributions and parameter errors. These findings highlight the importance of uncertainty analysis of drought assessments, given that most studies in climatology focus on extreme values for drought analysis.