Accurate estimation of snowpack dynamics is essential for predicting snowmelt-driven hydrological processes in mountainous regions. Although data assimilation techniques can integrate remote sensing observations into snow models to reduce uncertainties, their operational implementation remains constrained by sparse and irregular coverage of satellite data, complexity of mountainous terrain, and the computational demands of high-dimensional assimilation. In this study, an Observation-Masked Localized Ensemble Kalman Filter (OML-EnKF) framework was developed to restrict analysis updates to observed regions while preserving localized spatial covariance among nearby observations. This framework assimilated daily Copernicus snow cover fraction (SCF) observations into the Wflow_sbm distributed hydrological model across the Rhône River basin, France, for 2017–2019. We compared its performance against an open-loop run, a direct insertion scheme, and a one-dimensional EnKF. Results demonstrate that incorporating a snow module in the Wflow_sbm model improved seasonal discharge patterns. The OML-EnKF produced spatially coherent snow water equivalent patterns consistent with topography and improved snow depth estimates in mid-elevation regions. Both EnKF schemes refined SCF and improved discharge predictions across multiple gauged stations. Furthermore, temporal shifts in snowpack accumulation and ablation modulated runoff timing, with increased snowmelt contributions during winter (October–March) and diminished discharge in late spring (April–June). Sensitivity analyses showed that a moderate localization radius (0.025–0.1 (Formula presented.), approximately 2.8–11.1 km) effectively captured spatial correlations within the OML-EnKF, whereas excessively restrictive radius values degraded performance. These findings show that the OML-EnKF enhances snowpack characterization and discharge prediction in the Rhône River basin, offering a promising tool for water resource management in snow-dominated basins.

An adequate water quality prediction mobile system is crucial for real-time, proactive, and convenient water environment monitoring through mobile devices to reduce or prevent water environmental threats. After exploring the feasibility and superiority of the LSTM-seq2seq model for predicting various water quality indicators, the optimal time step range for different length predictions was proposed. To verify the generalizability and reusability of the model, the performance differences of migrating models was investigated. Based on the entire process, we have developed a cost-effective, widely applicable, and sustainable operational prediction system framework. It was successfully applied in the Huangshui River Basin for two years. Results indicated that the model can achieve an NSE of above 0.5 for indicators with high coefficient of variation and above 0.75 for more stable indicators. When carrying out transfer applications, the model can achieve an NSE performance of above 0.5 for most sites in short to medium-term forecasting.