Drought Assessment Using the Standardized Precipitation Index and Its Association with Climate Anomalies in Kotabumi, West Lampung
DOI:
https://doi.org/10.70211/ijesi.v2i1.215Keywords:
Climate Anomalies, Drought, El Niño, ENSO, La NiñaAbstract
This study assesses drought patterns in Kotabumi, West Lampung, Indonesia, using the Standardized Precipitation Index (SPI) at 1-month, 3-month, and 12-month time scales to analyze meteorological, seasonal, and hydrological droughts from 1999 to 2017. The research also explores the relationship between drought severity and global climate anomalies, particularly El Niño and La Niña (ENSO) events. Results show that short-term droughts commonly occur during the dry season (July–October), with several months experiencing extreme drought (SPI < -2.0), such as March 2016 and May 2017. Seasonal droughts, captured through SPI-3, revealed more persistent dry periods primarily in the second half of the year. Long-term analysis suggests that years like 2002, 2006, 2015, and 2016 were marked by sustained rainfall deficits. A clear correlation was found between SPI values and ENSO phases: El Niño years were associated with negative SPI values indicating drought, while La Niña years generally exhibited positive SPI values indicating wetter conditions. These findings demonstrate the effectiveness of SPI in drought monitoring and its utility in developing early warning systems and climate adaptation strategies in drought-prone regions.
References
Zhang, Y., Wang, P., Chen, Y., Yang, J., Wu, D., Ma, Y., Huo, Z., & Liu, S. (2023). The optimal time-scale of Standardized Precipitation Index for early identifying summer maize drought in the Huang-Huai-Hai region, China. Journal of Hydrology: Regional Studies, 46, 101350. https://doi.org/10.1016/j.ejrh.2023.101350 DOI: https://doi.org/10.1016/j.ejrh.2023.101350
Karurung, W. S., Lee, K., & Lee, W. (2025). Assessment of forest fire vulnerability prediction in Indonesia: Seasonal variability analysis using machine learning techniques. International Journal of Applied Earth Observation and Geoinformation, 138, 104435. https://doi.org/10.1016/j.jag.2025.104435 DOI: https://doi.org/10.1016/j.jag.2025.104435
Medida, S. K., Rani, P. P., Kumar, G. V. S., Sireesha, P. V. G., Kranthi, K. C., Vinusha, V., Sneha, L., Naik, B. S. S. S., Pramanick, B., Brestic, M., & Hossain, A. (2023). Detection of water deficit conditions in different soils by comparative analysis of standard precipitation index and normalized difference vegetation index. Heliyon, 9(4), e15093. https://doi.org/10.1016/j.heliyon.2023.e15093 DOI: https://doi.org/10.1016/j.heliyon.2023.e15093
Lorenzo, M. N., Pereira, H., Alvarez, I., & Dias, J. M. (2023). Standardized Precipitation Index (SPI) evolution over the Iberian Peninsula during the 21st century. Atmospheric Research, 297, 107132. https://doi.org/10.1016/j.atmosres.2023.107132 DOI: https://doi.org/10.1016/j.atmosres.2023.107132
Mashuri, Karlina, & Sujono, J. (2025). Assessment of satellite-based rainfall products for drought monitoring in the Siak Watershed, Indonesia. Environmental Challenges, 19, 101134. https://doi.org/10.1016/j.envc.2025.101134 DOI: https://doi.org/10.1016/j.envc.2025.101134
Alizadeh, O., & Mousavizadeh, M. (2025). Impact of ENSO on extreme precipitation in Southwest Asia. Global and Planetary Change, 244. https://doi.org/10.1016/j.gloplacha.2024.104645 DOI: https://doi.org/10.1016/j.gloplacha.2024.104645
Ansari, A., Pranesti, A., Telaumbanua, M., Alam, T., Taryono, Wulandari, R. A., Nugroho, B. D. A., & Supriyanta. (2023). Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach. In Heliyon (Vol. 9, Issue 9). Elsevier Ltd. https://doi.org/10.1016/j.heliyon.2023.e19639 DOI: https://doi.org/10.1016/j.heliyon.2023.e19639
Davey, M. K., Brookshaw, A., & Ineson, S. (2014). The probability of the impact of ENSO on precipitation and near-surface temperature. Climate Risk Management, 1, 5–24. https://doi.org/10.1016/j.crm.2013.12.002 DOI: https://doi.org/10.1016/j.crm.2013.12.002
Gourdel, R., Monasterolo, I., & Gallagher, K. (2025). Climate transition spillovers and sovereign risk: Evidence from Indonesia. Energy Economics, 143, 108211. https://doi.org/10.1016/j.eneco.2025.108211 DOI: https://doi.org/10.1016/j.eneco.2025.108211
Gradiyanto, F., Parmantoro, P. N., & Suharyanto. (2024). Impact of climate change on Kupang River flow and hydrological extremes in Greater Pekalongan, Indonesia. Water Science and Engineering. https://doi.org/10.1016/j.wse.2024.03.005 DOI: https://doi.org/10.1016/j.wse.2024.03.005
Haq, D. Z., Rini Novitasari, D. C., Hamid, A., Ulinnuha, N., Arnita, Farida, Y., Nugraheni, R. D., Nariswari, R., Ilham, Rohayani, H., Pramulya, R., & Widjayanto, A. (2021). Long Short-Term Memory Algorithm for Rainfall Prediction Based on El-Nino and IOD Data. Procedia Computer Science, 179, 829–837. https://doi.org/10.1016/j.procs.2021.01.071 DOI: https://doi.org/10.1016/j.procs.2021.01.071
Hidayat, R. (2016). Modulation of Indonesian Rainfall Variability by the Madden-julian Oscillation. Procedia Environmental Sciences, 33, 167–177. https://doi.org/10.1016/j.proenv.2016.03.067 DOI: https://doi.org/10.1016/j.proenv.2016.03.067
Kim, K., Chowdhury, R., Pant, P., Yamashita, E., & Ghimire, J. (2021). Assessment of ENSO risks to support transportation resilience. Progress in Disaster Science, 12. https://doi.org/10.1016/j.pdisas.2021.100196 DOI: https://doi.org/10.1016/j.pdisas.2021.100196
Kuswanto, H., & Naufal, A. (2019). Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods. MethodsX, 6, 1238–1251. https://doi.org/10.1016/j.mex.2019.05.029 DOI: https://doi.org/10.1016/j.mex.2019.05.029
Lestari, S., King, A., Vincent, C., Karoly, D., & Protat, A. (2019). Seasonal dependence of rainfall extremes in and around Jakarta, Indonesia. Weather and Climate Extremes, 24. https://doi.org/10.1016/j.wace.2019.100202 DOI: https://doi.org/10.1016/j.wace.2019.100202
Loo, Y. Y., Billa, L., & Singh, A. (2015). Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia. Geoscience Frontiers, 6(6), 817–823. https://doi.org/10.1016/j.gsf.2014.02.009 DOI: https://doi.org/10.1016/j.gsf.2014.02.009
Lubis, M. Z., Situmorang, E., Simanjuntak, A. V. H., Riama, N. F., Pasma, G. R., Dwinovantyo, A., Kausarian, H., Natih, N. M. N., Batara, Ansari, K., & Jamjareegulgarn, P. (2025). Indonesian Throughflow, spatial–temporal variability, and its relationship to ENSO events in the Lombok Strait. Egyptian Journal of Aquatic Research. https://doi.org/10.1016/j.ejar.2025.01.004 DOI: https://doi.org/10.1016/j.ejar.2025.01.004
Nugroho, B. D. A., & Nuraini, L. (2016). Cropping Pattern Scenario based on Global Climate Indices and Rainfall in Banyumas District, Central Java, Indonesia. Agriculture and Agricultural Science Procedia, 9, 54–63. https://doi.org/10.1016/j.aaspro.2016.02.124 DOI: https://doi.org/10.1016/j.aaspro.2016.02.124
Nur’utami, M. N., & Hidayat, R. (2016). Influences of IOD and ENSO to Indonesian Rainfall Variability: Role of Atmosphere-ocean Interaction in the Indo-pacific Sector. Procedia Environmental Sciences, 33, 196–203. https://doi.org/10.1016/j.proenv.2016.03.070 DOI: https://doi.org/10.1016/j.proenv.2016.03.070
Nuryanto, D. E., Pawitan, H., Hidayat, R., & Aldrian, E. (2016). Heavy Rainfall Distributions Over Java Sea in Wet Season. Procedia Environmental Sciences, 33, 178–186. https://doi.org/10.1016/j.proenv.2016.03.068 DOI: https://doi.org/10.1016/j.proenv.2016.03.068
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