A Methodological Approach for Selecting Climate Datasets for Drought Prediction Using ROC Analysis and Youden’s Index - JIMÉNEZ ESPINOSA , ESEQUIEL ROLANDO; DAVID, DONESSA (Journal of Agrometeorology, 2026)
This study presents a methodological approach for evaluating and selecting climate datasets for drought prediction in Guyana, based primarily on the combination of Receiver Operating Characteristic (ROC) analysis and Youden’s Index (J). Traditional hydrological metrics—Nash–Sutcliffe Efficiency (NSE) and Kling–Gupta Efficiency Modified (KGEM) were applied only as comparators to highlight their limitations for event detection. Ten climate datasets available in CARiDRO were assessed at two Standardized Precipitation Index (SPI) time scales—3 months (SPI3) and 12 months (SPI12) using records from two contrasting stations: Georgetown (coastal) and Timehri (inland). ROC–Youden rankings were stable across stations and time scales and revealed clear leaders. At Georgetown, the highest J values were obtained by RCP 4.5 for both SPI3 (J = 0.44) and SPI12 (J = 0.48), followed by aexsk (J = 0.22 and 0.34, respectively). At Timehri, aexsk achieved the highest performance for both SPI3 (J = 0.21) and SPI12 (J = 0.35), while RCP 4.5 showed strong but second-best performance only at SPI12 (J = 0.32). In contrast, NSE and KGEM yielded inconsistent rankings between SPI3 and SPI12, underscoring the limitations of continuous-value error metrics for classifying drought versus non-drought events. By prioritizing classification skill (true/false event discrimination) over aggregate fit, the ROC–Youden framework provides an operationally relevant basis for dataset selection in agricultural drought monitoring. The SPI3-focused findings are directly applicable to short-term crop-water decisions, whereas SPI12 results inform longer-term water allocation.