Accuracy Evaluation of Standardized Precipitation Index (SPI) Estimation under Conventional Assumption in Yeşilırmak, Kızılırmak, and Konya Closed Basins, Turkey

Abstract

The doubt in the calculation algorithm of the standardized precipitation index (SPI), which is widely preferred in the evaluation and monitoring of drought, still remains up-to-date because its calculation process is performed in the form of standardization or normalization with a default probability distribution. Therefore, the success of this index is directly affected by the choice of the probability distribution model. This study is based on the effect of three different parameter estimation methods on the calculation process, as well as the comparison of the SPI results calculated based on the default Gamma distribution and the distribution with the best ability to represent the 3-and 12-month consecutive summed rainfall data among the 15 candidate distributions namely Gamma (GAM), Generalized Extreme Value (GEV), Pearson Type III (P III), Log Pearson Type III (LP III), two-parameter Lognormal (LN2), three-parameter Lognormal (LN3), Generalized Logistic (GLOG), Extreme Value Type I (EVI), Generalized Pareto (GPAR), Weilbul (W), Normal (N), Exponential (EXP), Logistic (LOG), four-parameter Wakeby (WK4), and five-parameter Wakeby (WK5) distributions. Approximately 68.4% and 18.4% of the 3-month data considered had the best fit to the Weibull and Pearson III distribution, while approximately 24% and 18% of the 12-month data had the best fit to the Weibull and Logistic distribution. On the other hand, it was found that the default Gamma distribution calculated the extreme drought categories significantly more than the best-fit distribution model. In terms of parameter estimation methods, L-moments for 3-month series and maximum likelihood approaches for 12-month series were most dominant.

An appraisal of the local‐scale spatio‐temporal variations of drought based on the integrated GRACE/GRACE‐FO observations and fine‐resolution FLDAS model

“…The KB is the second largest basin of the country with a drainage area of 82 221 km 2 where about 4 million people reside in 18 provinces (FMP, 2019). The basin receives an estimated precipitation of 455 mm per annum with an air temperature of 13.7°C (Hinis & Geyikli, 2023). There are six sub‐basins in the basin, including the Upper Kizilirmak Basin (UKB), Lower Kizilirmak Basin (LKB), Central Kizilirmak Basin (CKB), Delice Basin (DB), Seyfe Closed Basin (SCB), and Develi Closed Basin (DCB) (Figure 1).…”

Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation

“…It has been conducted by scientist to identify many droughts in terms of various region. For example, Hinis and Geyikli, (2023) discussed the accuracy of SPI predictions using several candidate distributions in Yeşilırmak, Kızılırmak and Konya Closed Basins. They revealed that L-moments for 3month series and maximum likelihood approaches for 12-month series were most strong.…”