Table 1 Comparison of AUC and AIC results of logistic regression analyses of models with fixed variables (models A to E) along with the final model (model F) shown in Fig. 3A, which was achieved by stepwise variable selection.

A higher AUC shows better model prediction performance, whereas a lower AIC is indicative of a simpler, more effective model. The associated hazard maps are provided in fig. S4.

ModelPredictor variable(s)AUCAIC
AFluvisols (probability)0.69787 ± 5
BIrrigated area0.72739 ± 7
CAridity, slope (binary, 0.1°)0.73716 ± 8
DSlope (binary, 0.1°), soil pH0.77688 ± 9
EAridity, Holocene fluvial sediments
(binary), slope (binary, 0.1°)
0.77664 ± 8
FFluvisols (probability), Holocene fluvial
sediments (binary), soil organic
carbon, soil pH, slope (binary, 0.1°)
0.80644 ± 9