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Article

Developing an Agricultural Drought Prediction Framework for Timor-Leste

1
Science Advanced-Global Challenges Program, Monash University, Melbourne 3800, Australia
2
Climate Risk and Early Warning Systems (CREWS), Science and Innovation Group, Bureau of Meteorology, Melbourne 3008, Australia
3
School of Earth, Atmosphere and Environment, Monash University, Melbourne 3800, Australia
4
School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne 3000, Australia
*
Author to whom correspondence should be addressed.
Climate 2026, 14(9), 194; https://doi.org/10.3390/cli14090194 (registering DOI)
Submission received: 21 July 2026 / Revised: 8 September 2026 / Accepted: 9 September 2026 / Published: 13 September 2026
(This article belongs to the Special Issue Climate and Weather Extremes (Third Edition))

Abstract

Agricultural drought is a natural hazard which has disastrous impacts on populations, economies and the environment in drought-vulnerable countries. This study develops an agricultural drought prediction framework for Timor-Leste—a least developed country in Southeast Asia. Currently, long-range forecasting capabilities and proactive agricultural drought management practices in Timor-Leste remain limited, constraining the country’s ability to prepare for and respond to periodic drought events. This research evaluated the effectiveness of drought prediction in Timor-Leste using seasonal rainfall outlooks from a dynamical Global Climate Model (GCM): the European Centre for Medium-Range Weather Forecasts’ (ECMWF) Seasonal Forecast System 5 (SEAS5), across different drought and non-drought events. SEAS5 effectively predicted an increased probability of below-average median rainfall over Timor-Leste for the case study of the 2015–2016 El Niño-induced drought and was assessed as having higher probabilistic skill across the wider study area compared with the Australian Bureau of Meteorology’s (BoM) Australian Community Climate and Earth-System Simulator (ACCESS-S2). While some limitations exist in raw forecast skill at times of year when predictability is lower, outlooks from both GCMs could, with sound communication, be applied to predict drought’s onset, peak and end. This research serves as a foundational step toward the development of an agricultural drought early warning system in Timor-Leste.
Keywords: agricultural drought; dynamical global climate models (GCMs); seasonal rainfall prediction; early warning system (EWS); Timor-Leste agricultural drought; dynamical global climate models (GCMs); seasonal rainfall prediction; early warning system (EWS); Timor-Leste

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MDPI and ACS Style

Edney, S.; Watkins, A.B.; Kuleshov, Y. Developing an Agricultural Drought Prediction Framework for Timor-Leste. Climate 2026, 14, 194. https://doi.org/10.3390/cli14090194

AMA Style

Edney S, Watkins AB, Kuleshov Y. Developing an Agricultural Drought Prediction Framework for Timor-Leste. Climate. 2026; 14(9):194. https://doi.org/10.3390/cli14090194

Chicago/Turabian Style

Edney, Sasha, Andrew B. Watkins, and Yuriy Kuleshov. 2026. "Developing an Agricultural Drought Prediction Framework for Timor-Leste" Climate 14, no. 9: 194. https://doi.org/10.3390/cli14090194

APA Style

Edney, S., Watkins, A. B., & Kuleshov, Y. (2026). Developing an Agricultural Drought Prediction Framework for Timor-Leste. Climate, 14(9), 194. https://doi.org/10.3390/cli14090194

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