Does a tradeoff between temporal stability and sampling frequency contribute to the prediction accuracy of soil moisture in alternative stable states?

• Revealed sampling frequency vs. temporal stability trade-off in SWC prediction. • Low-frequency sampling (15–45d) with indirect stable-site selection kept R 2 > 0.75. • Shrubland was sampling-frequency independent, grassland required higher resolution. • Indirect stable-location identification surpassed direct methods in forest/shrubland soils. • Optimal sampling depends on vegetation heterogeneity and its frequency response. Soil water content (SWC) regulates patchy vegetation patterns in arid regions, where alternative stable states (ASS) explain vegetation mosaics. Although temporal stability and sampling frequency (SF) are critical for SWC prediction, their tradeoff and its impact on prediction accuracy remain poorly understood. Using SWC data from 48 sampling occasions at 70 cm depth across grassland, shrubland, and forest ecosystems, we examined how SF influences SWC dynamics and prediction accuracy. Results showed that SF significantly affected SWC dynamics and temporal stability, particularly under lower SFs (15–45 days, LSFs) compared to higher SFs (≤7 days, HSFs). Under HSFs, mean SWC remained stable across vegetation types, whereas under LSFs, significant effects emerged except in specific grassland layers. Temporal stability indices—including Spearman’s rank correlation coefficient, mean relative difference range, and representative location values—were generally higher under HSFs. Despite this, SWC was accurately predicted across all vegetation types and soil layers under LSFs (R2 > 0.75, p < 0.01). Moreover, indirect prediction methods significantly outperformed direct methods. These findings reveal a vegetation-dependent tradeoff between SF and temporal stability: forests retain high predictability under LSFs, while grasslands require HSFs for accurate estimation. This hydrological distinction offers insight into the stability mechanisms underlying alternative vegetation states within ASS frameworks. Our study informs optimized SWC monitoring strategies and advances process-based understanding of ASS formation and maintenance in arid ecosystems.