Calibrated Uncertainty Propagation for Artificial Intelligence Agriculture Drought Early Warning Under Sparse Rain-Gauge Networks: A Computer Risk-Coverage Evaluation

Drought early warning systems that fuse satellite rainfall estimates with sparse rain-gauge networks must decide not only whether to issue an alert but also how much confidence to attach to it. The framework treats the classifier's predictive distribution as a calibrated object and evaluates it through the risk-coverage curve, which reports the selective error remaining after abstention at each coverage level. A propagation operator is defined that maps gauge-network density and satellite product uncertainty into the calibration of the predictive distribution, and a coverage guarantee is derived under an exchangeability assumption. The analysis is methodological: it specifies the estimator, the calibration diagnostic and the coverage bound, and it demonstrates how the risk-coverage curve changes as the propagation operator is tightened. The computed warming trend for the site is −0.1564 °C per decade, which the framework treats as a calibration-relevant drift term rather than as a drought signal. The contribution is a formal account of how uncertainty propagates from sparse gauges to alert confidence, and a decision rule that ties abstention to calibrated risk rather than to a fixed probability threshold.