Monthly runoff prediction is crucial for water resource allocation and flood prevention. Many existing methods use identical deep learning networks to understand monthly runoff patterns, neglecting the importance of predictor selection. To enhance predictive accuracy and reliability, this study proposes an RFECV–SSA–LSTM forecasting approach. It iteratively eliminates predictors derived from SSA decomposition and PACF using recursive feature elimination and cross-validation (RFECV) to identify the most relevant subset for predicting the target flow. LSTM modeling is then used to forecast flows 1–7 months into the future. Furthermore, the RFECV–SSA framework complements any machine-learning-based runoff prediction method. To demonstrate the method’s reliability and effectiveness, its outputs are compared across three scenarios: direct LSTM, MIR–LSTM, and RFECV–LSTM, using monthly runoff historical data from Yangxian and Hanzhong hydrological stations in the Hanjiang River Basin, China. The results show that the RFECV–LSTM method is more robust and efficient than the direct LSTM and MIR–LSTM counterparts, with the smallest number of outliers for NSE, NRMSE, and PPTS under all forecasting scenarios. The MIR–LSTM approach exhibits the worst performance, indicating that single-metric-based feature selection may eliminate valuable information. The SSA time–frequency decomposition is superior, with NSE values remaining stably around 0.95 under all scenarios. The NSE value of the RFECV–SSA–LSTM method is greater than 0.95 under almost all forecasting scenarios, outperforming other benchmark models. Therefore, the RFECV–SSA–LSTM method is effective for forecasting highly nonlinear runoff series, exhibiting high accuracy and generalization ability.

Drought poses substantial threats to terrestrial ecosystems, yet whether vegetation vulnerability patterns systematically diverge between soil moisture and meteorological droughts—and how long-term hydrological adaptation governs ecosystem susceptibility to rare extreme droughts—remains poorly understood. Here, we systematically quantify vegetation responses to soil moisture drought (Standardized Soil Moisture Index, SSI) and meteorological drought (3-month Standardized Precipitation Evapotranspiration Index, SPEI-3) across the Yellow River Basin from 1982 to 2022, examining resistance, resilience, and peak loss alongside their spatial differentiation and temporal lags. SSI and SPEI-3 identified droughts exhibit distinct event characteristics: meteorological droughts occur more frequently with shorter durations, whereas soil moisture droughts persist 2–3 times longer once initiated, consistent with the buffering and memory effects of soil water storage. Vegetation response patterns diverge substantially between drought definitions, with SSI-derived resistance showing stronger associations with drought duration, whereas SPEI-3 conditions elevate the importance of drought-period climate. Wetter regions and deciduous-mixed forests exhibit the highest standardized peak losses despite comparable or higher resistance, consistent with the hypothesis that vegetation adapted to long-term water-abundant conditions may be more susceptible to rare extremes. Random Forest and SHapley Additive exPlanations analyses demonstrate that resistance is associated with drought attributes and drought-period climatic conditions, resilience is strongly associated with drought-period temperature and post-drought climate conditions, and peak loss shows the strongest background environmental dependence. Temporal lags between peak vegetation loss and drought intensity vary spatially, with stronger heterogeneity under SSI than SPEI-3. Our findings demonstrate that comprehensive vulnerability assessment requires integrating complementary drought metrics and multidimensional response frameworks, providing critical insights for improving drought monitoring, ecological risk assessment, and sustainable ecosystem management under climate change.