<![CDATA[In recent years, a broad consensus has emerged that global hydroclimatic anomalies have increased rainfall variability in tropical cities. Addressing this, we simulated and predicted rainfall patterns in Jakarta’s Ciliwung River Basin using a hierarchical framework that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA) time series modeling with GIS spatial interpolation. Monthly rainfall records from three stations, Kemayoran (1990–2020), Halim (1990–2018), and Tanjung Priok (1990–2015), were analyzed. Following Ljung-Box and residual tests, the SARIMA (0,1,1)(0,1,1)₁₂ model was selected. Accuracy verification yielded Mean Absolute Percentage Error (MAPE) values below 10% for all stations: 8.4% (Kemayoran), 7.9% (Halim), and 8.1% (Tanjung Priok). Core predictions indicate that by 2030, rainfall intensity will continue declining across all stations, while seasonal rainfall rhythms remain stable. Spatial rainfall pattern comparison between 2025 and 2030 reveals a northward shift of the high rainfall concentration zone. This study has two limitations: only three sample stations were used, and no formal uncertainty quantification was performed for spatial predictions, making the reliability of spatial patterns weaker than suggested by visualizations. Despite these constraints, the proposed statistical-spatial modeling approach supports flood preparedness and adaptive water resource management in data-constrained urban watersheds, provided users account for the stated methodological limitations.]]>
Copyrights © 2026
