@article{Luanmuang2026, 
author = {Kritdilada Luanmuang and Manad KhamKong and Nawapon Nakharutai and Pimwarat Srikummoon},
title = {Optimal distribution selection and spatial drought modeling in upper northern Thailand using an integrated actual precipitation index and regular vine copula framework},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {6},
pages = {16788-16810},
keywords = {actual precipitation index, climate resilience, drought classification, extreme rainfall modeling, regular vine copula, standardized precipitation index, sustainable water management},
url = {https://www.sciopen.com/article/10.3934/math.2026689},
doi = {10.3934/math.2026689},
abstract = {The standardized precipitation index (SPI) is a foundational tool for drought assessment. However, its application is constrained by complex probability distribution selection and mandatory normal transformation. To overcome these limitations, this study introduces a systematic framework that uses the Anderson–Darling test to optimize distribution selection. This approach refines drought evaluation through the actual precipitation index (API). By using untransformed precipitation data, the API enables a more direct climatic assessment. Results demonstrated strong consistency between the API and SPI across upper northern Thailand. Both indices successfully detected the 2011 regional floods and the 2015-2016 El Niño drought. Comparatively, the API demonstrated superior sensitivity to moisture saturation, particularly in Chiang Mai, Nan, and Phayao. Furthermore, spatial dynamic analysis using regular vine (R-vine) copulas identified Lampang as the primary regional hub. Lampang governs the central cluster (Chiang Mai, Lamphun, and Phrae) and mediates dependencies between Phayao and Chiang Rai. Nevertheless, localized geographic interactions create substantial concurrent extreme rainfall risks for the Chiang Mai–Lamphun pair. Because it preserves physical rainfall units, the API facilitates more actionable risk management than the SPI. Consequently, integrating R-vine copulas within the API framework is strongly recommended. This integration enhances spatial rainfall modeling, early warning systems, and adaptive water resource management, ultimately supporting climate resilience and sustainable development in vulnerable regions.}
}