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Pemetaan Kerentanan dan Peramalan Kekeringan Meteorologis di DAS Blega Menggunakan Indeks China-Z Multiskala dan Model SARIMA Azizah Affandy, Nur; Prayoga, Dimas Prayoga; Dhana, Rio Rahma; Iranata, Data
Nucleus Journal Vol. 5 No. 1 (2026): May (In Progress)
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/nucleus.v5i1.5106

Abstract

Meteorological drought is one of the hydrometeorological phenomena that frequently occurs and has the potential to cause serious impacts on water availability, agricultural activities, and the sustainability of community livelihoods. This condition requires a deeper understanding of drought patterns as well as the capability to anticipate the likelihood of future drought events. This study aims to analyze the spatio-temporal dynamics of meteorological drought in the Blega Watershed using the China-Z Index (CZI) at multiple time scales, namely CZI-1, CZI-3, CZI-6, CZI-12, and CZI-24, while also mapping areas vulnerable to drought. In addition, this study develops drought forecasting using the Autoregressive Integrated Moving Average (ARIMA) model to estimate future drought trends. The results indicate that the CZI is capable of capturing fluctuations in drought conditions from wet to dry categories across different time scales and identifying areas with varying levels of drought vulnerability within the Blega Watershed. Time series modeling shows that the SARIMA (0,0,0)(1,0,1)12 model provides the best predictive performance, with a Mean Absolute Error (MAE) value of 0.599, indicating that the model is reasonably accurate in representing drought dynamics. These findings suggest that drought patterns in the study area are influenced by annual seasonal variations closely related to rainfall distribution. Therefore, this study not only provides a scientific overview of meteorological drought characteristics in the Blega Watershed but also offers valuable information for drought mitigation planning, water resource management, and more responsive policy-making toward climate variability.