Vermicomposting has been recommended as an eco-friendly method to transform organic waste into nutrient resources with minimum energy input. However, odor and pest issues associated with this method limit the use of vermicomposting, especially in indoor conditions. This study evaluated the effectiveness of applying hypochlorous acid (HOCl) to deodorize the vermicomposting process and improve the breeding environment for earthworms (Eisenia fetida). The deodorization performance of HOCl was compared by measuring the amount of ammonia (NH₃) and amine (R-NH₂) released from the decaying process of two types of food waste: HOCl-treated (HTW) waste and non-treated waste (NTW). The total and individual weights of earthworms in the waste treated with HOCl was measured to evaluate the impact on earthworm reproduction after applying HOCl. The results showed that HOCl application could reduce NH₃ by 40% and R-NH₂ by 80%, and increase the earthworm population size and total weight by up to 29% and 92%, respectively, compared to the control group. These results suggest that HOCl application is potentially an efficient method to control the odor and to boost earthworm reproduction and thus facilitate vermicomposting for improved food waste treatment and environmental quality.

Continuous radar surface velocity records require purpose-specific conditioning because the temporal information available for processing differs between real-time monitoring and retrospective analysis. Short-period fluctuations and spikes can obscure stage-related flow responses under both settings. We evaluated a stepwise quality control framework that separates these two processing roles using 10 min records from six monitoring sites in South Korea. Causal preprocessing combined Huber-weighted recursive least squares, fuzzy correction, and a trailing Hampel filter to generate a provisional series. Retrospective processing applied a centered Hampel filter followed by criterion-based zero-phase moving average smoothing. Causal preprocessing reduced the standard deviation of successive velocity increments by 14.1–53.4%, with a further 1.4–7.8% reduction observed after centered filtering. A three-point moving-average window was selected at all sites, retaining 98.4–99.9% of the peak velocity and a velocity sum ratio of 1.000. Three sites satisfied all the selection criteria, two satisfied the shape retention criteria, and one was retained under a flagged fallback because the increment variance and slope criteria were not met. Postprocessed index-velocity-method-derived hydrographs showed a lower RMSE and higher R² when compared to the operational stage–discharge benchmark at all sites, while signed biases varied by site. The proposed framework provides a traceable pathway from observation availability and data status to shape assessment and downstream discharge evaluation.

Complete daily streamflow time series are essential for sustainable water resources management and reliable hydrological modelling; however, even short data gaps can substantially reduce the usability of streamflow records. Recurrent missing data may lead to inefficient model calibration, decreased reliability of peak and low-flow estimates, and biased hydrological statistics. Therefore, rather than leaving missing values unfilled, it can be beneficial to infill daily streamflow using appropriate methods and to provide flags indicating imputed periods. In South Korea, streamflow monitoring prior to 2008 primarily focused on flood-related observations, resulting in relatively limited daily streamflow records; since then, the production of continuous daily streamflow data for water resources management has expanded. As of 2024, daily streamflow records from more than 420 gauging stations are managed and disseminated, yet a non-negligible number of stations still contain missing values due to various causes such as river works and uncertainties in stage–discharge relationships associated with the operation of hydraulic structures. This study comparatively evaluates gap-filling techniques using paired upstream–downstream gauging stations located in basins with diverse rainfall regimes and hydrological characteristics. We assess conventional methods widely used in practice (scaling, linear regression, and equi-percentile/quantile-based approaches) under different missing-data conditions and benchmark them against an extended long short-term memory (extended LSTM) time-series model designed for streamflow infilling. Performance is evaluated using the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS). In addition, flow duration curves (FDCs) are compared to examine each method’s ability to reproduce the post-infilling flow regime distribution. The outcomes are expected to support condition-dependent selection of gap-filling strategies and to improve the reliability of daily streamflow datasets with explicit quality flags.