Drought Assessment Using the Standardized Precipitation Index and Its Association with Climate Anomalies in Kotabumi, West Lampung

M Rizky Ismail; Chandra Bogireddy; Siti Anugrah Mulya Putri Ofrial; Tiara

doi:10.70211/ijesi.v2i1.215

Authors

M Rizky Ismail Universitas Lampung, Indonesia Indonesia
Chandra Bogireddy Shantou University, China China
Siti Anugrah Mulya Putri Ofrial Universitas Lampung, Indonesia Indonesia
Tiara Universitas Lampung, Indonesia Indonesia

DOI 10.70211/ijesi.v2i1.215

Article Integrity

Article Metrics

199 Article Views 347 PDF Downloads 0 Crossref Citations

Abstract

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.

Keywords

Climate Anomalies Drought El Niño ENSO La Niña

References

1

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

2

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

3

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

4

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

5

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

6

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

7

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

8

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

9

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

10

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

11

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

12

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

13

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

14

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

15

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

16

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

17

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

18

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

19

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

20

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

Author Biographies

M Rizky Ismail

Universitas Lampung, Indonesia Indonesia

Enviromental Engineering Program

Chandra Bogireddy

Shantou University, China China

Departement Of Civil And Environmental Engineering

Siti Anugrah Mulya Putri Ofrial

Universitas Lampung, Indonesia Indonesia

Civil Engineering Program

Tiara

Universitas Lampung, Indonesia Indonesia

Enviromental Engineering Program

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Drought Assessment Using the Standardized Precipitation Index and Its Association with Climate Anomalies in Kotabumi, West Lampung

Authors

Abstract

Keywords

References

Author Biographies

M Rizky Ismail

Chandra Bogireddy

Siti Anugrah Mulya Putri Ofrial

Tiara

License

Download Article

Downloads

Article Information

Published

Issue

Article Tools

Citation Impact

Bagian Kanan

Submissions